Professor Geoffrey McLachlan

Featured projects	Duration
Theoretical aspects of semisupervised learning

McLachlan, Geoffrey J. and Krishnan, Thriyambakam (2008). The EM algorithm and extensions. 2nd ed. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470191613

McLachlan, G. J., Do, K. and Ambroise, C (2004). Analyzing Microarray Gene Expression Data. New York: Wiley-Interscience.

McLachlan, Geoffrey J., Do, Kim-Anh and Ambroise, Christophe (2004). Analyzing microarray gene expression data. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/047172842x

McLachlan, G. J. and Peel, D. (2000). Finite Mixture Models. New York: John Wiley & Sons.

McLachlan, Geoffrey and Peel, David (2000). Finite mixture models: McLachlan/finite mixture models. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/0471721182

McLachlan, Geoffrey J. and Krishnan, Thriyambakam (1997). The EM algorithm and extensions. New York, United States: Wiley.

McLachlan, Geoffrey John (1992). Discriminant analysis and statistical pattern recognition. New York , United States: Wiley.

McLachlan, Geoffrey J. (1992). Discriminant analysis and statistical pattern recognition. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/0471725293

McLachlan, Geoffrey J. and Basford, Kaye E. (1988). Mixture models : inference and applications to clustering. New York, United States: Marcel Dekker.

Lee, S.X. and McLachlan, G.J. (2021). On mean and/or variance mixtures of normal distributions. Statistical learning and modeling in data analysis: methods and applications. (pp. 1-10) edited by S. Balzano, G.C. Porzio, R. Salvatore, D. Vistocco and M. Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69944-4_13

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2021). Automated gating and dimension reduction of high-dimensional cytometry data. Mathematical, computational and experimental T cell immunology. (pp. 281-294) edited by Carmen Molina-París and Grant Lythe . Cham, Switzerland: Springer. doi: 10.1007/978-3-030-57204-4_16

McLachlan, Geoffrey and Ahfock, Daniel (2021). Estimation of classification rules from partially classified data. Data analysis and rationality in a complex world. (pp. 149-157) edited by Theodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari and Rebecca Nugent. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-60104-1_17

McLachlan, G. J., Rathnayake, S. and Lee, S. X. (2020). Comprehensive chemometrics: chemical and biochemical data analysis. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 267-304) edited by Steven Brown, Roma Tauler and Beata Walczak. Oxford, United Kingdom: Elsevier.

McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2019). Mixture of factor analyzers for the clustering and visualization of high-dimensional data. Advances in latent class analysis: a festschrift in honor of C. Mitchell Dayton. (pp. 79-98) edited by Gregory R. Hancock, Jeffrey R. Harring and George B. Macready. Charlotte, NC, United States: Information Age Publishing.

Lee, Sharon X. and McLachlan, Geoffrey J. (2018). Risk measures based on multivariate skew normal and skew t-mixture models. Asymmetric dependence in finance: diversification, correlation and portfolio management in market downturns. (pp. 152-168) edited by Jamie Alcock and Stephen Satchell. Chichester, West Sussex, United Kingdom: John Wiley & Sons. doi: 10.1002/9781119288992.ch7

McLachlan, G. J., Bean, R. W. and Ng, S. K. (2017). Clustering. Bioinformatics Vol. II: Structure, Function, and Applications. (pp. 345-362) edited by Jonathan M. Keith. New York, NY, United States: Humana Press. doi: 10.1007/978-1-4939-6613-4_19

Nguyen, Hien D., McLachlan, Geoffrey J. and Hill, Michelle M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. Proteome bioinformatics. (pp. 109-117) edited by Shivakumar Keerthikumar and Suresh Mathivanan. New York, NY United States: Humana Press. doi: 10.1007/978-1-4939-6740-7_9

Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. Data science, innovative developments in data analysis and clustering. (pp. 43-57) edited by Francesco Palumbo, Angela Montanari and Maurizio Vichi. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6

Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Disease Modelling and Public Health, Part A. (pp. 75-102) edited by Arni S.R. Srinivasa Rao, Saumyadipta Pyne and C.R. Rao. Amsterdam, Netherlands: Elsevier. doi: 10.1016/bs.host.2017.08.005

McLachlan, Geoffrey J. (2016). Mixture distributions - further developments. Wiley statsref: statistics reference online. (pp. 1-13) Chichester, United Kingdom: John Wiley & Sons. doi: 10.1002/9781118445112.stat00947.pub2

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Application of mixture models to large datasets. Big data analytics: methods and applications. (pp. 57-74) edited by Saumyadipta Pyne, B. L. S. Prakasa Rao and S. B. Rao. New Delhi, India: Springer India. doi: 10.1007/978-81-322-3628-3_4

McLachlan, Geoffrey J. and Rathnayake, Suren I. (2016). Mixture models for standard p-dimensional Euclidean data. Handbook of cluster analysis. (pp. 145-171) edited by Christian Hennig, Marina Meila, Fionn Murtagh and Roberto Rocci. Boca Raton, FL, United States: CRC Press. doi: 10.1201/b19706-14

McLachlan, Geoffrey (2015). Multivariate Analysis: Classification and Discrimination. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 116-120) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42150-1

McLachlan, Geoffrey J. (2015). Mixture Models in Statistics. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 624-628) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42055-6

McLachlan, Geoffrey J. (2015). Computation: Expectation-Maximization Algorithm. International Encyclopedia of the Social & Behavioral Sciences: Second Edition. (pp. 469-474) Amsterdam, Netherlands: Elsevier . doi: 10.1016/B978-0-08-097086-8.42007-6

McLachlan, G. J., Flack, L. K., Ng, S. K. and Wang, K. (2013). Clustering of gene expression data via normal mixture models. Statistical methods for microarray data analysis: methods and protocols. (pp. 103-119) edited by Andrei Y. Yakovlev, Lev Klebanov and Daniel Gaile. New York, NY, United States: Humana Press. doi: 10.1007/978-1-60327-337-4_7

Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2012). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 139-172) edited by James E. Gentle, Wolfgang Karl Hardle and Yuichi Mori. Berlin & New York: Springer. doi: 10.1007/978-3-642-21551-3__6

McLachlan, G. J. (2012). An enduring interest in classification: supervised and unsupervised. Journeys to data mining: experiences from 15 renowned researchers. (pp. 147-171) edited by Mohamed Medhat Gaber. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-28047-4_12

Ng, Shu Kay, Krishnan, Thriyambakam and McLachlan, Geoffrey J. (2011). The EM Algorithm. Handbook of Computational Statistics. (pp. 139-172) Berlin, Germany: Springer. doi: 10.1007/978-3-642-21551-3_6

McLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2011). Mixtures of factor analyzers for the analysis of high-dimensional data. Mixture estimation and applications. (pp. 189-212) edited by Kerrie L. Mengersen, Christian P. Robert and D. Michael Titterington. Chichester, United Kingdom: John Wiley and Sons. doi: 10.1002/9781119995678.ch9

Ng, Shu-Kay and McLachlan, Geoffrey J. (2010). Expert networks with mixed continuous and categorical feature variables: A location modeling approach.. Machine learning research progress. (pp. 355-368) edited by Hannah Peters and Mia Vogel. New York, U.S.A.: Nova Science.

McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. Classification as a Tool for Research: Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation. (pp. 177-184) edited by Hermann Locarek-Junge and Claus Weihs. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-10745-0

McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of high-dimensional data via finite mixture models. Advances in Data Analysis, Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft für Klassifikation e.V., Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification Society (VOC Helmut-Schmidt-University, Hamburg, July 16–18, 2008. (pp. 33-44) edited by Andreas Fink, Berthold Lausen, Wilfried Seidel and Alfred Ultsch. Heidelberg, Germany: Springer-Verlag. doi: 10.1007/978-3-642-01044-6

McLachlan, Geoffrey J., Ng, Shu-Kay and Wang, K. (2010). Clustering of high-dimensional and correlated data. Data Analysis and Classification: Proceedings of the 6th Conference of the Classification and Data Analysis Group of the SocietàItaliana di Statistica, Macerata, Italy 12-14 September, 2007. (pp. 3-11) edited by Francesco Palumbo, Carlo Natale Lauro and Michael J. Greenacre. Berlin; Heidelberg, Germany: Springer - Verlag. doi: 10.1007/978-3-642-03739-9_1

Flack, L. K. and McLachlan, G. J. (2009). Clustering methods for gene-expression data. Handbook of Research on Systems Biology Applications in Medicine. (pp. 209-220) edited by Andriani Daskalaki. United States: IGI Global. doi: 10.4018/978-1-60566-076-9.ch011

McLachlan, G. J. (2009). Model-based clustering. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 655-681) edited by Steven D. Brown, Roma Tauler and Beata Walczak. Oxford, U.K.: Elsevier Science. doi: 10.1016/B978-044452701-1.00068-5

Le Cao, Kim-Anh and McLachlan, Geoffrey J. (2009). Statistical analysis on microarray data: selection of gene prognosis signatures. Computational biology: issues and applications in oncology. (pp. 55-76) edited by Tuan Pham. New York, United States: Springer. doi: 10.1007/978-1-4419-0811-7_3

McLachlan, G. J. and Ng, S-K. (2009). EM. The Top Ten Algorithms in Data Mining. (pp. 93-115) edited by Wu, X. and Kumar, V.. Florida, United States: Chapman & Hall/CRC. doi: 10.1201/9781420089653-12

McLachlan, G. J., Bean, R. W. and Ng, S.-K. (2008). Clustering. Bioinformatics, volume 2: Structure, function and applications. (pp. 423-439) edited by J. M. Keith. New Jersey, United States: Humana Press. doi: 10.1007/978-1-60327-429-6_22

McLachlan, G J., Chevelu, J. and Zhu, J. (2008). Correcting for Selection Bias via Cross-Validation in the Classification of Microarray Data. Beyond Parametrics in Interdisciplinary Research: Festschrift in Honor of Professor Pranab K. Sen. (pp. 364-376) edited by Balakrishnan, N., Pena, E. A. and Silvapulle, M. J.. United States: Institute of Mathematical Statistics. doi: 10.1214/193940307000000284

McLachlan, Geoffrey J., Ng, Angus and Bean, Richard W. (2008). Clustering of microarray data via mixture models. Statistical advances in the biomedical sciences: clinical trials, epidemiology, survival analysis, and bioinformatics. (pp. 365-383) edited by Atanu Biswas, Sujay Datta, Jason P. Fine and Mark R. Segal. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470181218.ch21

Jones, L., Ng, S., Ambroise, C, Monico, K. A., Khan, N. and McLachlan, G. J. (2005). Use of microarray data via model-based classification in the study and prediction of survival from lung cancer. Methods of microarray data analysis IV. (pp. 163-173) edited by Jennifer S. Shoemaker and Simon M. Lin. New York, USA: Springer. doi: 10.1007/0-387-23077-7_13

Ng, S. K., Krishnan, T. and McLachlan, G. J. (2004). The EM algorithm. Handbook of Computational Statistics: Concepts and Methods. (pp. 137-168) edited by J.E. Gentle, W. Hardle and Y. Mori. Germany: Springer-Verlag.

McLachlan, G. J., Ng, A.S. K. and Peel, D. (2003). On clustering by mixture models. Exploratory Data Analysis in Empirical Research. (pp. 141-148) edited by M. Schwaiger and O. Opitz. Germany: Springer. doi: 10.1007/978-3-642-55721-7_16

Chamroukhi, Faïcel, Pham, Nhat Thien, Hoang, Van Hà and McLachlan, Geoffrey J. (2024). Functional mixtures-of-experts. Statistics and Computing, 34 (3) 98. doi: 10.1007/s11222-023-10379-0

Lyu, Ziyang, Ahfock, Daniel, Thompson, Ryan and McLachlan, Geoffrey J. (2024). Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R. Australian and New Zealand Journal of Statistics, 66 (2), 146-162. doi: 10.1111/anzs.12413

Lee, Sharon X. and McLachlan, Geoffrey J. (2024). An overview of skew distributions in model-based clustering. Science Talks, 9 100298, 100298. doi: 10.1016/j.sctalk.2024.100298

Koh, Edwin J.Y., Amini, Eiman, Spier, Carlos A., McLachlan, Geoffrey J., Xie, Weiguo and Beaton, Nick (2024). A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy. Minerals Engineering, 205 108481, 1-16. doi: 10.1016/j.mineng.2023.108481

Maleki, Mohsen, McLachlan, Geoffrey J. and Lee, Sharon X. (2023). Robust clustering based on finite mixture of multivariate fragmental distributions. Statistical Modelling, 23 (3), 247-272. doi: 10.1177/1471082X211048660

Ahfock, Daniel and McLachlan, Geoffrey J. (2023). Semi-supervised learning of classifiers from a statistical perspective: a brief review. Econometrics and Statistics, 26, 124-138. doi: 10.1016/j.ecosta.2022.03.007

Nguyen, Hien D., Fryer, Daniel and McLachlan, Geoffrey J. (2023). Order selection with confidence for finite mixture models. Journal of the Korean Statistical Society, 52 (1), 154-184. doi: 10.1007/s42952-022-00195-z

Wang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition, 133 108989, 1-9. doi: 10.1016/j.patcog.2022.108989

Ng, Shu Kay, Tawiah, Richard, McLachlan, Geoffrey J. and Gopalan, Vinod (2023). Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach. Biostatistics, 24 (1), 108-123. doi: 10.1093/biostatistics/kxab037

Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D. and McLachlan, Geoffrey J. (2023). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods, 52 (14), 1-12. doi: 10.1080/03610926.2021.2002360

Koh, Edwin J. Y., Amini, Eiman, Gaur, Shruti, Becerra Maquieira, Miguel, Jara Heck, Christian, McLachlan, Geoffrey J. and Beaton, Nick (2022). An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models. Minerals Engineering, 189 107886, 107886. doi: 10.1016/j.mineng.2022.107886

Farzammehr, Mohadeseh Alsadat and McLachlan, Geoffrey J. (2022). A spatial heterogeneity mixed model with skew-elliptical distributions. Communications for Statistical Applications and Methods, 29 (3), 373-391. doi: 10.29220/csam.2022.29.3.373

Ahfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2022). Statistical file-matching of non-Gaussian data: a game theoretic approach. Computational Statistics and Data Analysis, 168 107387, 1-16. doi: 10.1016/j.csda.2021.107387

Lee, Sharon X. and McLachlan, Geoffrey J. (2022). An overview of skew distributions in model-based clustering. Journal of Multivariate Analysis, 188 104853, 1-14. doi: 10.1016/j.jmva.2021.104853

Nguyen, Hien Duy, Nguyen, TrungTin, Chamroukhi, Faicel and McLachlan, Geoffrey John (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications, 8 (1) 13. doi: 10.1186/s40488-021-00125-0

Koh, Edwin J. Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy. Minerals Engineering, 173 107230, 107230. doi: 10.1016/j.mineng.2021.107230

Koh, Edwin J.Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations. Minerals Engineering, 170 107026, 1-11. doi: 10.1016/j.mineng.2021.107026

Ahfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2021). Data fusion using factor analysis and low-rank matrix completion. Statistics and Computing, 31 (5) 58. doi: 10.1007/s11222-021-10033-7

Lee, Sharon X., McLachlan, Geoffrey J. and Leemaqz, Kaleb L. (2021). Multi‐node expectation–maximization algorithm for finite mixture models. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14 (4) sam.11529, 297-304. doi: 10.1002/sam.11529

Farzammehr, M. A., Mohammadzadeh, M, Zadkarami, M. R. and McLachlan, G. J. (2021). Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors. Communications in Statistics: Theory and Methods, 51 (24), 1-22. doi: 10.1080/03610926.2021.1897843

Ahfock, Daniel and McLachlan, Geoffrey J. (2021). Harmless label noise and informative soft-labels in supervised classification. Computational Statistics and Data Analysis, 161 107253, 107253. doi: 10.1016/j.csda.2021.107253

Lee, Sharon X. and McLachlan, Geoffrey J. (2021). On formulations of skew factor models: Skew factors and/or skew errors. Statistics and Probability Letters, 168 108935, 108935. doi: 10.1016/j.spl.2020.108935

Ahfock, Daniel and McLachlan, Geoffrey J. (2020). An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified. Statistics and Computing, 30 (6), 1779-1790. doi: 10.1007/s11222-020-09971-5

Lee, Sharon X., Lin, Tsung-I and McLachlan, Geoffrey J. (2020). Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Advances in Data Analysis and Classification, 15 (2), 481-512. doi: 10.1007/s11634-020-00420-9

Maleki, M. , McLachlan, G. J. , Gurewitsch, R. , Aruru, M. and Pyne, S. (2020). A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities. Statistics and Applications, 18 (1), 295-306.

Nguyen, T. Tin, Nguyen, Hien D., Chamroukhi, Faicel and McLachlan, Geoffrey J. (2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics and Statistics, 7 (1). doi: 10.1080/25742558.2020.1750861

Nguyen, Hien D., Forbes, Florence and McLachlan, Geoffrey J. (2020). Mini-batch learning of exponential family finite mixture models. Statistics and Computing, 30 (4), 731-748. doi: 10.1007/s11222-019-09919-4

Tawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2020). A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction. Biometrics, 76 (3) biom.13202, 753-766. doi: 10.1111/biom.13202

Nguyen, Hien D. and McLachlan, Geoffrey (2019). On approximations via convolution-defined mixture models. Communications in Statistics - Theory and Methods, 48 (16), 3945-3955. doi: 10.1080/03610926.2018.1487069

Nguyen, Hien D., Yee, Yohan, McLachlan, Geoffrey J. and Lerch, Jason P. (2019). False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study. SORT, 43 (2), 1-22. doi: 10.2436/20.8080.02.87

Tawiah, Rchard, Yau, Kelvin K. W., McLachlan, Geoffrey J., Chambers, Suzanne and Ng, Shu-Kay (2019). A multilevel survival model with random covariates and unobservable random effects. Statistics in Medicine, 38 (6), 1036-1055. doi: 10.1002/sim.8041

McLachlan, Geoffrey J., Lee, Sharon X. and Rathnayake, Suren I. (2019). Finite mixture models. Annual Review of Statistics and Its Application, 6 (1), 355-378. doi: 10.1146/annurev-statistics-031017-100325

Farzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza and McLachlan, Geoffrey J. (2019). Skew-normal generalized spatial panel data model. Communications in Statistics: Simulation and Computation, 50 (11), 1-29. doi: 10.1080/03610918.2019.1622718

Farzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza, McLachlan, Geoffrey J. and Lee, Sharon X. (2019). Skew-normal Bayesian spatial heterogeneity panel data models. Journal of Applied Statistics, 47 (5), 1-23. doi: 10.1080/02664763.2019.1657812

Tawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2019). Mixture cure models with time-varying and multilevel frailties for recurrent event data. Statistical Methods in Medical Research, 29 (5) ARTN 0962280219859377, 096228021985937-1385. doi: 10.1177/0962280219859377

Ng, Shu-Kay, Tawiah, Richard and McLachlan, Geoffrey J. (2018). Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework. Pattern Recognition, 88, 261-271. doi: 10.1016/j.patcog.2018.11.022

Nguyen, Hien D., Wang, Dianhui and McLachlan, Geoffrey J. (2018). Randomized mixture models for probability density approximation and estimation. Information Sciences, 467, 135-148. doi: 10.1016/j.ins.2018.07.056

Jones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). logKDE: log-transformed kernel density estimation. Journal of Open Source Software, 3 (28) 870, 870. doi: 10.21105/joss.00870

Nguyen, Hien D., Jones, Andrew T. and McLachlan, Geoffrey J. (2018). Stream-suitable optimization algorithms for some soft-margin support vector machine variants. Japanese Journal of Statistics and Data Science., 1 (1), 81-108. doi: 10.1007/s42081-018-0001-y

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2018). A Block EM Algorithm for Multivariate Skew Normal and Skew t-Mixture Models. IEEE Transactions on Neural Networks and Learning Systems, 29 (99) 8310916, 1-11. doi: 10.1109/TNNLS.2018.2805317

Lloyd-Jones, Luke R., Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). A globally convergent algorithm for a lasso-penalized mixture of linear regression models. Computational Statistics and Data Analysis, 119, 19-38. doi: 10.1016/j.csda.2017.09.003

Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). Chunked-and-averaged estimators for vector parameters. Statistics and Probability Letters, 137, 336-342. doi: 10.1016/j.spl.2018.02.051

Lee, Sharon X. and McLachlan, Geoffrey J. (2018). EMMIXcskew: an R package for the fitting of a mixture of canonical fundamental skew t-distributions. Journal of Statistical Software, 83 (3). doi: 10.18637/jss.v083.i03

Nguyen, Hien D., Ullmann, Jeremy F. P., Mclachlan, Geoffrey J., Voleti, Venkatakaushik, Li, Wenze, Hillman, Elizabeth M. C., Reutens, David C. and Janke, Andrew L. (2017). Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modeling. Statistical Analysis and Data Mining, 11 (1), 5-16. doi: 10.1002/sam.11366

Viroli, Cinzia and McLachlan, Geoffrey J. (2017). Deep Gaussian mixture models. Statistics and Computing, 29 (1), 1-9. doi: 10.1007/s11222-017-9793-z

Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Some theoretical results regarding the polygonal distribution. Communications in Statistics: Theory and Methods, 47 (20), 5083-5095. doi: 10.1080/03610926.2017.1386312

Lee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Handbook of Statistics, 36, 75-102.

Lin, Tsung-I, Wang, Wan-Lun, McLachlan, Geoffrey J. and Lee, Sharon X. (2017). Robust mixtures of factor analysis models using the restricted multivariate skew-t distribution. Statistical Modelling, 18 (1), 50-72. doi: 10.1177/1471082X17718119

Nguyen, Hien D., McLachlan, Geoffrey J., Orban, Pierre, Bellec, Pierre and Janke, Andrew L. (2017). Maximum pseudolikelihood estimation for model-based clustering of time series data. Neural Computation, 29 (4), 990-1020. doi: 10.1162/NECO_a_00938

Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A universal approximation theorem for mixture-of-experts models. Neural Computation, 28 (12), 2585-2593. doi: 10.1162/NECO_a_00892

Ahfock, Daniel, Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Partial identification in the statistical matching problem. Computational Statistics and Data Analysis, 104, 79-90. doi: 10.1016/j.csda.2016.06.005

Lloyd-Jones, Luke R., Nguyen, Hien D., Mclachlan, Geoﬀrey J., Sumpton, Wayne and Wang, You-Gan (2016). Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics, 72 (4), 1255-1265. doi: 10.1111/biom.12531

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Progress on a conjecture regarding the triangular distribution. Communications in Statistics: Theory and Methods, 46 (22), 11261-11271. doi: 10.1080/03610926.2016.1263742

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Linear mixed models with marginally symmetric nonparametric random effects. Computational Statistics and Data Analysis, 103, 151-169. doi: 10.1016/j.csda.2016.05.005

Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Spatial clustering of time series via mixture of autoregressions models and Markov random fields. Statistica Neerlandica, 70 (4), 414-439. doi: 10.1111/stan.12093

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Maximum likelihood estimation of triangular and polygonal distributions. Computational Statistics and Data Analysis, 102, 23-36. doi: 10.1016/j.csda.2016.04.003

McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Comment on "On nomenclature for, and the relative merits of, two formulations of skew distributions," by A. Azzalini, R. Browne, M. Genton, and P. McNicholas. Statistics & Probability Letters, 116, 1-5. doi: 10.1016/j.spl.2016.04.004

Nguyen, Hien D., Lloyd-Jones, Luke R. and McLachlan, Geoffrey J. (2016). A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23 (8) 7501879, 1131-1135. doi: 10.1109/LSP.2016.2586180

Lee, Sharon X and McLachlan, Geoffrey J (2016). Finite mixtures of canonical fundamental skew t-distributions: The unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing, 26 (3), 573-589. doi: 10.1007/s11222-015-9545-x

Nguyen, Hien D., McLachlan, Geoffrey J., Ullmann, Jeremy F. P. and Janke, Andrew L. (2016). Laplace mixture autoregressive models. Statistics and Probability Letters, 110, 18-24. doi: 10.1016/j.spl.2015.11.006

Lee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2016). Modeling of inter-sample variation in flow cytometric data with the joint clustering and matching procedure. Cytometry Part A, 89 (1), 30-43. doi: 10.1002/cyto.a.22789

Nguyen, Hien D., McLachlan, Geoffrey J. and Wood, Ian A. (2016). Mixtures of spatial spline regressions for clustering and classification. Computational Statistics and Data Analysis, 93, 76-85. doi: 10.1016/j.csda.2014.01.011

Lin, Tsung-I, McLachlan, Geoffrey J. and Lee, Sharon X. (2016). Extending mixtures of factor models using the restricted multivariate skew-normal distribution. Journal of Multivariate Analysis, 143, 398-413. doi: 10.1016/j.jmva.2015.09.025

Aghaeepour, Nima, Chattopadhyay, Pratip, Chikina, Maria, Dhaene, Tom, Van Gassen, Sofie, Kursa, Miron, Lambrecht, Bart N., Malek, Mehrnoush, McLachlan, G. J., Qian, Yu, Qiu, Peng, Saeys, Yvan, Stanton, Rick, Tong, Dong, Vens, Celine, Walkowiak, Slawomir, Wang, Kui, Finak, Greg, Gottardo, Raphael, Mosmann, Tim, Nolan, Garry P., Scheuermann, Richard H. and Brinkman, Ryan R. (2016). A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytometry Part A, 89 (1), 16-21. doi: 10.1002/cyto.a.22732

Nguyen, Hien D. and McLachlan, Geoffrey J. (2016). Laplace mixture of linear experts. Computational Statistics and Data Analysis, 93, 177-191. doi: 10.1016/j.csda.2014.10.016

Tian, Ting, McLachlan, Geoffrey J., Dieter, Mark J. and Basford, Kaye E. (2015). Application of multiple imputation for missing values in three-way three-mode multi-environment trial data. PLoS One, 10 (12) e0144370, e0144370.1-e0144370.25. doi: 10.1371/journal.pone.0144370

Ingrassia, Salvatore, McLachlan, Geoffrey J. and Govaert, Gerard (2015). Special issue on "New trends on model-based clustering and classification". Advances in Data Analysis and Classification, 9 (4), 367-369. doi: 10.1007/s11634-015-0224-8

Nguyen, Hien D. and McLachlan, Geoffrey J. (2015). Maximum likelihood estimation of Gaussian mixture models without matrix operations. Advances in Data Analysis and Classification, 9 (4), 371-394. doi: 10.1007/s11634-015-0209-7

Pyne, Saumyadipta, Lee, Sharon X. and McLachlan, Geoffrey J. (2015). Nature and man: the goal of bio-security in the course of rapid and inevitable human development. Journal of the Indian Society of Agricultural Statistics, 69 (2), 117-125.

Ng, Shu Kay, McLachlan, Geoffrey J., Wang, Kui, Nagymanyoki, Zoltan, Liu, Shubai and Ng, Shu-Wing (2015). Inference on differences between classes using cluster-specific contrasts of mixed effects. Biostatistics, 16 (1), 98-112. doi: 10.1093/biostatistics/kxu028

Lin, Tsung-I, Wu, Pal H., McLachlan, Geoffrey J. and Lee, Sharon X. (2014). A robust factor analysis model using the restricted skew-t distribution. Test, 24 (3), 510-531. doi: 10.1007/s11749-014-0422-2

McLachlan, Geoffrey J. and Rathnayake, Suren (2014). On the number of components in a Gaussian mixture model. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery, 4 (5), 341-355. doi: 10.1002/widm.1135

Pyne, Saumyadipta, Lee, Sharon X., Wang, Kui, Irish, Jonathan, Tamayo, Pablo, Nazaire, Marc-Danie, Duong, Tarn, Ng, Shu-Kay, Hafler, David, Levy, Ronald, Nolan, Garry P., Mesirov, Jill and McLachlan, Geoffrey J. (2014). Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLoS One, 9 (7) e100334, e100334.1-e100334.11. doi: 10.1371/journal.pone.0100334

Nguyen, Hien D., McLachlan, Geoffrey J., Cherbuin, Nicolas and Janke, Andrew L. (2014). False discovery rate control in magnetic resonance imaging studies via Markov random fields. IEEE Transactions on Medical Imaging, 33 (8) 6811158, 1735-1748. doi: 10.1109/TMI.2014.2322369

Lee, Sharon and McLachlan, Geoffrey J. (2014). Finite mixtures of multivariate skew t-distributions: Some recent and new results. Statistics and Computing, 24 (2), 181-202. doi: 10.1007/s11222-012-9362-4

Ng S.K. and McLachlan G.J. (2014). Mixture models for clustering multilevel growth trajectories. Computational Statistics and Data Analysis, 71, 43-51. doi: 10.1016/j.csda.2012.12.007

Boehning, Dankmar, Hennig, Christian, McLachlan, Geoffrey J. and McNicholas, Paul D. (2014). The 2nd special issue on advances in mixture models. Computational Statistics and Data Analysis, 71, 1-2. doi: 10.1016/j.csda.2013.10.010

Lee S.X. and McLachlan G.J. (2013). EMMIXuskew: An R package for Fitting Mixtures of Multivariate Skew t distributions via the EM algorithm. Journal of Statistical Software, 55 (12), 1-22. doi: 10.18637/jss.v055.i12

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Model-based clustering and classification with non-normal mixture distributions. Statistical Methods and Applications, 22 (4), 427-454. doi: 10.1007/s10260-013-0237-4

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Rejoinder to the discussion of "Model-based clustering and classification with non-normal mixture distributions". Statistical Methods and Applications, 22 (4), 473-479. doi: 10.1007/s10260-013-0249-0

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). On mixtures of skew normal and skew t-distributions. Advances in Data Analysis and Classification, 7 (3), 241-266. doi: 10.1007/s11634-013-0132-8

McLachlan, G. J. (2013). How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification: written contribution to the discussion on the paper by Hennig and Liao. Applied Statistics-Journal of the Royal Statistical Society Series C, 62 (3), 309-369. doi: 10.1111/j.1467-9876.2012.01066.x

Aghaeepour, Nima, Finak, Greg, Hoos, Holger, Mosmann, Tim R., Brinkman, Ryan, Gottardo, Raphael, Scheuermann, Richard H., The FlowCAP Consortium, McLachlan, Geoffrey J., Wang, Kui and The DREAM Consortium (2013). Critical assessment of automated flow cytometry analysis techniques. Nature Methods, 10 (3), 228-238. doi: 10.1038/nmeth.2365

Basford, Kaye E., McLachlan, Geoffrey J. and Rathnayake, Suren I. (2013). On the classification of microarray gene-expression data. Briefings in Bioinformatics, 14 (4) bbs056, 402-410. doi: 10.1093/bib/bbs056

Wang, Kui, Ng, Shu Kay and McLachlan, Geoffrey J. (2012). Clustering of time-course gene expression profiles using normal mixture models with autoregressive random effects. Bmc Bioinformatics, 13 (1) 300, 300.1-300.14. doi: 10.1186/1471-2105-13-300

McLachlan, Geoffrey J. (2012). Discriminant analysis. Wiley Interdisciplinary Reviews: Computational Statistics., 4 (5), 421-431. doi: 10.1002/wics.1219

Schroder, Kate, Irvine, Katharine M., Taylor, Martin S., Bokil, Nilesh J., Le Cao, Kim-Anh, Masterman, Kelly-Anne, Labzin, Larisa I., Semple, Colin A., Kapetanovic, Ronan, Fairbairn, Lynsey, Akalin, Altuna, Faulkner, Geoffrey J., Baillie, John Kenneth, Gongora, Milena, Daub, Carsten O., Kawaji, Hideya, McLachlan, Geoffrey J., Goldman, Nick, Grimmond, Sean M., Carninci, Piero, Suzuki, Harukazu, Hayashizaki, Yoshihide, Lenhard, Boris, Hume, David A. and Sweet, Matthew J. (2012). Conservation and divergence in Toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages. Proceedings of the National Academy of Sciences of the USA, 109 (16), E944-E953. doi: 10.1073/pnas.1110156109

Melli, Gabor, Wu, Xindong, Beinat, Paul, Bonchi, Francesco, Cao, Longbing, Duan, Rong, Faloutsos, Christos, Ghani, Rayid, Kitts, Brendan, Goethals, Bart, McLachlan, Geoff, Pei, Jian, Srivastava, Ashok and Zaiane, Osmar (2012). Top-10 data mining case studies. International Journal of Information Technology and Decision Making, 11 (2), 389-400. doi: 10.1142/S021962201240007X

Nikulin, V, Huang, TH, Ng, SK, Rathnayake, SI and McLachlan, GJ (2011). A very fast algorithm for matrix factorization. Statistics and Probability Letters, 81 (7), 773-782. doi: 10.1016/j.spl.2011.02.001

Baek, Jangsun and McLachlan, Geoffrey J. (2011). Mixtures of common t-factor analyzers for clustering high-dimensional microarray data. Bioinformatics, 27 (9) btr112, 1269-1276. doi: 10.1093/bioinformatics/btr112

Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2011). Classification of high-dimensional microarray data with a two-step procedure via a Wilcoxon criterion and multilayer perceptron. International Journal of Computational Intelligence and Applications, 10 (1), 1-14. doi: 10.1142/S1469026811002969

McLachlan, Geoffrey J. (2011). Commentary on Steinley and Brusco (2011): Recommendations and cautions. Psychological Methods, 16 (1), 80-81. doi: 10.1037/a0021141

Zhao, Yun, Lee, Andy H., Yau, Kelvin K.W. and McLachlan, Geoffrey J. (2011). Assessing the adequacy of Weibull survival models: a simulated envelope approach. Journal of Applied Statistics, 38 (10), 2089-2097. doi: 10.1080/02664763.2010.545115

McLachlan, G. J. and Rathnayake, S. I. (2011). Testing for Group Structure in High-Dimensional Data. Journal of Biopharmaceutical Statistics, 21 (6), 1113-1125. doi: 10.1080/10543406.2011.608342

Baek, Jangsun, McLachlan, Geoffrey J. and Flack, Lloyd K. (2010). Mixtures of factor analyzers with common factor loadings: Applications to the clustering and visualization of high-dimensional data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (7) 5184847, 1298-1309. doi: 10.1109/TPAMI.2009.149

Le Cao, Kim-Anh, Meugnier, Emmanuelle and McLachlan, Geoffrey J. (2010). Integrative mixture of experts to combine clinical factors and gene markers. Bioinformatics, 26 (9) btq107, 1192-1198. doi: 10.1093/bioinformatics/btq107

Tang, Liangdan, Yang, Junzheng, Ng, Shu-Kay, Rodriguez, Noah, Choi, Pui-Wah, Vitonis, Allison, Wang, Kui, McLachlan, Geoffrey J., Caiazzo, Robert J., Liu, Brian C.-S., Welch, Brian C.-S., Cramer, Daniel W., Berkowitz, Ross S. and Ng, Shu-Wing (2010). Autoantibody profiling to identify biomarkers of key pathogenic pathways in mucinous ovarian cancer. European Journal of Cancer, 46 (1), 170-179. doi: 10.1016/j.ejca.2009.10.003

Zhao, Yun, Lee, Andy H., Yau, Kelvin K. W., Burke, Valerie and McLachlan, Geoffrey J. (2009). A score test for assessing the cured proportion in the long-term survivor mixture model. Statistics In Medicine, 28 (27), 3454-3466. doi: 10.1002/sim.3696

Pyne, S., Hu, X., Wang, K., Rossin, E., Lin, T.-I., Maier, L. M., Baecher-Allan, C., McLachlan, G. J., Tamayo, P., Hafler, D. A., De Jager, P. L. and Mesirow, J. P. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences of the United States of America, 106 (21), 8519-8524. doi: 10.1073/pnas.0903028106

Suarez, E., Burguete, A. and McLachlan, G. J. (2009). Microarray data analysis for differential expression: a tutorial. Puerto Rico Health Sciences Journal, 28 (2), 89-104.

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. Journal of Machine Learning Research, 7, 89-100.

Jorgensen, Murray A. and McLachlan, Geoffrey J. (2008). Wallace's approach to unsupervised learning: The Snob program. The Computer Journal, 51 (5), 571-578. doi: 10.1093/comjnl/bxm121

McLachlan, Geoff J., Wang, Kent and Ng, Shu Kay (2008). Large-scale simultaneous inference with applications to the detection of differential expression with microarray data (with discussion). Statistica, 68 (1), 1-30. doi: 10.6092/issn.1973-2201/3525

McLachlan, Geoffrey J., Wang, K. and Ng, S. K. (2008). Comments on: Augmenting the bootstrap to analyze high dimensional genomic data. Test, 17 (1), 43-46. doi: 10.1007/s11749-008-0106-x

Agrawal, M. C., Caudill, Steven B., Chakraborti, S., Draper, Norman, Dryden, Ian, Gani, Joe, Gilmour, Steven G., Govindarajulu, Z., Hand, David J., Franses, Philip Hans, Kacker, Raghu, Khamis, Harry, Khuri, Andre I., Lewis, Toby, Mardia, Kanti, McLachlan, Geoff, Naik, Dayanand, Prescott, Phil, Kumar, V. S. Sampath, Tomizawa, Sadao and Wynn, Henry (2008). Professor Gopal Kanji's retirement as editor of Journal of Applied Statistics. Journal of Applied Statistics, 35 (1), 1-8. doi: 10.1080/02664760701814495

McLaren, C. E., Gordeuk, V. R., Chen, W. -P., Barton, J. C., Action, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J. and Bean, R. (2008). Bivariate mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African Americans, Hispanics, and whites in the Hemochromatosis and Iron Overload Screening (HEIRS) Study. Translational Research, 151 (2), 97-109. doi: 10.1016/j.trsl.2007.10.002

Caprani, C. C., O'Brien, E. J. and McLachlan, G. J. (2008). Characteristic Traffic Load Effects from a Mixture of Loading Events on Short to Medium Span Bridges. Structural Safety, 30 (3), 394-404. doi: 10.1016/j.strusafe.2006.11.006

Zhu, J. X., McLachlan, G. J., Jones, L. B. T. and Wood, I. A. (2008). On selection biases with prediction rules formed from gene expression data. Journal of Statistical Planning and Inference, 138 (2), 374-386. doi: 10.1016/j.jspi.2007.06.003

Wu, X., Kumar, V., Quinlan, J. R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G. J., Ng, A., Liu, B., Yu, P. S., Zhou, Z. H., Steinbach, M., Hand, D. J. and Steinberg, D. (2008). Top 10 Algorithms in Data Mining. Knowledge and Information Systems, 14 (1), 1-37. doi: 10.1007/s10115-007-0114-2

Wang, Kui, Yau, Kelvin K. W., Lee, Andy H. and McLachlan, Geoffrey J. (2007). Two-component Poisson Mixture Regression Modelling of Count Data With Bivariate Random Effects. Mathematical and Computer Modelling, 46 (11-12), 1468-1476. doi: 10.1016/j.mcm.2007.02.003

Do, K. A., McLachlan, G. J., Bean, R. W. and Wen, S. (2007). Application of gene shaving and mixture models to cluster microarray gene expression data. Cancer Informatics, 5, 25-43. doi: 10.1177/117693510700500002

Ng, S. K. and McLachlan, G. J. (2007). Extension of Mixture-of-Experts Networks for Binary Classification of Hierarchical Data. Artificial Intelligence in Medicine, 41 (1), 57-67. doi: 10.1016/j.artmed.2007.06.001

Suarez, E., Sariol, C. A., Burguete, A. and McLachlan, G. J. (2007). A tutorial in genetic epidemiology and some considerations in statistical modeling. Puerto Rico Health Sciences Journal, 26 (4), 401-421.

Baek, J., Son, Y. S. and McLachlan, G. J. (2007). Segmentation and intensity estimation of microarray images using a gamma-t mixture model. Bioinformatics, 23 (4), 458-465. doi: 10.1093/bioinformatics/btl630

McLachlan, G. J., Bean, R. W. and Jones, L. B. T. (2007). Extension of the Mixture of Factor Analyzers Model to Incorporate the Multivariate t-Distribution. Computational Statistics & Data Analysis, 51 (11), 5327-5338. doi: 10.1016/j.csda.2006.09.015

Lenzenweger, M. F., McLachlan, G. J. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling. Journal of Abnormal Psychology, 116 (1), 16-29. doi: 10.1037/0021-843X.116.1.16

Wang, Kui, Yau, Kelvin K. W., Lee, Andy H. and McLachlan, Geoffrey J. (2007). Multilevel Survival Modelling of Recurrent Urinary Tract Infections. Computer Methods and Programs in Biomedicine, 87 (3), 225-229. doi: 10.1016/j.cmpb.2007.05.013

Lee, Andy H., Wang, Kui, Yau, Kelvin K. W., McLachlan, Geoffrey J. and Ng, Shu Kay (2007). Maternity length of stay modelling by Gamma mixture regression with random effects. Biometrical Journal, 49 (5), 750-764. doi: 10.1002/bimj.200610371

Xiang, L., Lee, A. H., Yau, K. K. W. and McLachlan, G. J. (2007). A Score Test for Overdispersion in Zero-Inflated Poisson Mixed Regression Model. Statistics in Medicine, 26 (7), 1608-1622. doi: 10.1002/sim.2616

Jones, L. B. T., Bean, R., McLachlan, G. J. and Zhu, J. X. (2006). Mixture models for detecting differentially expressed genes in microarrays. International Journal of Neural Systems, 16 (5), 353-362. doi: 10.1142/S0129065706000755

Xiang, Liming, Lee, Andy H., Yau, Kelvin K. W. and McLachlan, Geoffrey J. (2006). A Score Test for Zero-Inflation in Correlated Count Data. Statistics In Medicine, 25 (10), 1660-1671. doi: 10.1002/sim.2308

McLachlan, G J, Ng, S K and Bean, R W (2006). Robust cluster analysis via mixture models. Austrian Journal of Statistics, 35 (2 & 3), 157-174.

Zhu, X., Ambroise, C and McLachlan, G J (2006). Selection bias in working wit the top genes in supervised classification of tissue samples. Statistical Methodology, 3 (1), 29-41. doi: 10.1016/j.stamet.2005.09.011

Lee, AH, Wang, K, Scott, JA, Yau, KKW and McLachlan, GJ (2006). Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods In Medical Research, 15 (1), 47-61. doi: 10.1191/0962280206sm429oa

Ng, S. K., McLachlan, G. J. and Lee, A. H. (2006). An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization. Artificial Intelligence In Medicine, 36 (3), 257-267. doi: 10.1016/j.artmed.2005.07.003

Ng, SK, McLachlan, GJ, Wang, K, Jones, LBT and Ng, SW (2006). A Mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics, 22 (14), 1745-1752. doi: 10.1093/bioinformatics/btl165

McLachlan, GJ, Bean, RW and Jones, LBT (2006). A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics, 22 (13), 1608-1615. doi: 10.1093/bioinformatics/btl148

Kerr, R. J., McLachlan, G. J. and Henshall, J. M. (2005). Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis. Genetics Selection Evolution, 37 (1), 83-103. doi: 10.1051/gse:2004037

McLachlan, G. J., Bean, R. W., Jones, L. and Zhu, J. X. (2005). Using mixture models to detect differentially expressed genes. Australian Journal Of Experimental Agriculture, 45 (7-8), 859-866. doi: 10.1071/EA05051

Bean, R and McLachlan, G (2005). Cluster analysis of high-dimensional data: A case study. Intelligent Data Engineering And Automated Learning Ideal 2005, Proceedings, 3578 (-), 302-310.

Ng, S. K., McLachlan, G. J., Yau, K. K. W. and Lee, A. H. (2004). Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects Adjustment. Statistics In Medicine, 23 (17), 2729-2744. doi: 10.1002/sim.1840

Ng, Shu-Kay and McLachlan, Geoffrey J. (2004). Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition, 37 (8), 1573-1589. doi: 10.1016/j.patcog.2004.02.012

Ng, S. K. and McLachlan, G. J. (2004). Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification. IEEE Transactions on Neural Networks, 15 (3), 738-749. doi: 10.1109/TNN.2004.826217

McLachlan, G. J. and Chang, S. U. (2004). Mixture modelling for cluster analysis. Statistical Methods In Medical Research, 13 (5), 347-361. doi: 10.1191/0962280204sm372ra

McLachlan, GJ and Khan, N (2004). On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samples. Journal of Multivariate Analysis, 90 (1), 90-105. doi: 10.1016/j.jmva.2004.02.002

Hand, DJ, Glasbey, C, Husmeier, D, Gower, JC, van Houwelingen, HC, Bugrien, JB, Nason, G, Critchley, F, Hoff, PD, McLachlan, GJ and Bean, RW (2004). Clustering objects on subsets of attributes - Discussion. Journal of The Royal Statistical Society Series B-statistical Methodology, 66 (4), 839-849.

Mar, J.C. and McLachlan, G.J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. International Journal of Software Engineering and Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482

Mar, J. C. and McLachlan, G. J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. International Journal of Software Engineering And Knowledge Engineering, 13 (6), 579-592. doi: 10.1142/S0218194003001482

Ng, S. K. and McLachlan, G. J. (2003). An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks Data. Statistics In Medicine, 22 (7), 1097-1111. doi: 10.1002/sim.1371

Ng, S. K. and McLachlan, G. J. (2003). On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures. Statistics And Computing, 13 (1), 45-55. doi: 10.1023/A:1021987710829

McLachlan, G. J., Peel, D. and Bean, R. W. (2003). Modelling High-Dimensional Data by Mixtures of Factor Analyzers. Computational Statistics & Data Analysis, 41 (3-4), 379-388. doi: 10.1016/S0167-9473(02)00183-4

Ng, A.S. K. and McLachlan, G. J. (2003). On some variants of the EM algorithm for the fitting of finite mixture models. Austrian Journal of Statistics, 32 (1 & 2), 143-161.

Ambroise, Christophe and McLachlan, Geoffrey J. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences of the United States of America, 99 (10), 6562-6566. doi: 10.1073/pnas.102102699

McLachlan, GJ, Bean, RW and Peel, D (2002). A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18 (3), 413-422. doi: 10.1093/bioinformatics/18.3.413

McLachlan, G. J. and Hamaty, K. L. (2002). Nearest-neighbor variance estimation (NNVE): Robust covariance estimation via nearest-neighbor cleaning - Comment. Journal of The American Statistical Association, 97 (460), 1009-1011. doi: 10.1198/016214502388618807

Cadez, I. V., Smyth, P., McLachlan, G. J. and McLaren, C. E. (2002). Maximum Likelihood Estimation of Mixture Densities for Binned and Truncated Multivariate Data. Machine Learning, 47 (1), 7-34. doi: 10.1023/A:1013679611503

Ng, S. K., O'Brien, M. F., Harrocks, S. N. and McLachlan, G. J. (2002). Influence of patient age and implantation technique on the probability of re-replacement of the homograft aortic valve. Journal of Heart Valve Disease, 11 (2), 217-223.

Rose, Stephen E., Chalk, Jonathan B., Griffin, Mark P., Janke, Andrew L., Chen, Fang, Mclachlan, Geoffrey J., Peel, David, Zelaya, Fernando O., Markus, Hugh S., Jones, Derek K., Simmons, Andrew, O'Sullivan, Michael, Jarosz, Jo M., Strugnell, Wendy and Doddrell, David M. (2001). MRI based diffusion and perfusion predictive model to estimate stroke evolution. Magnetic Resonance Imaging, 19 (8), 1043-1053. doi: 10.1016/S0730-725X(01)00435-0

McLachlan, G. J. (2001). Letter to the editor. Journal of Agricultural, Biological, and Environmental Statistics, 6 (2), 302-304. doi: 10.1198/108571101750524797

Peel, D, Whiten, WJ and McLachlan, GJ (2001). Fitting mixtures of Kent distributions to aid in joint set identification. Journal of The American Statistical Association, 96 (453), 56-63. doi: 10.1198/016214501750332974

Peel, D. and McLachlan, G. J. (2000). Robust Mixture Modelling Using the t Distribution. Statistics and Computing, 10 (4), 339-348. doi: 10.1023/A:1008981510081

Welham, J., McLachlan, G., Davies, G. and McGrath, J. (2000). Heterogeneity in schizophrenia; mixture modelling of age-at-first-admission, gender and diagnosis. Acta Psychiatrica Scandinavica, 101 (4), 312-317. doi: 10.1034/j.1600-0447.2000.101004312.x

Welham, J., McLachlan, G. J., Davies, G. and McGrath, J. J. (2000). Heterogeneity in schizophrenia; mixture modelling of age-at-first admission, gender and diagnosis. Acta Pyschiatrica Scandinavica, 1, 312-317.

McLaren, C. E., Kambour, E. L., McLachlan, G. J., Lukaski, H. C., Li, X., Brittenham, G. M. and McLaren, G. D. (2000). Patient-specific Analysis of Sequential Haematological Data by Multiple Linear Regression and Mixture Distribution Modelling. Statistics in Medicine, 19 (1), 83-98. doi: 10.1002/(SICI)1097-0258(20000115)19:1<83::AID-SIM246>3.0.CO;2-A

McLachlan, G. J., Peel, D., Basford, K. E. and Adams, P. (1999). The EMMIX software for the fitting of mixtures of normal and t-components. Journal of Statistical Software, 4 (2).

Ng, SK, McLachlan, GJ, McGiffin, DC and OBrien, MF (1999). Constrained mixture models in competing risks problems. Environmetrics, 10 (6), 753-767. doi: 10.1002/(SICI)1099-095X(199911/12)10:6<753::AID-ENV388>3.3.CO;2-B

Ng, SK and McLachlan, GJ (1998). On modifications to the long-term survival mixture model in the presence of competing risks. Journal of Statistical Computation And Simulation, 61 (1-2), 77-96. doi: 10.1080/00949659808811903

McLachlan, G (1998). 25 years of applied statistics. Journal of Applied Statistics, 25 (1), 3-22.

McLachlan, G (1998). Mathematical classification and clustering.. Psychometrika, 63 (1), 93-95. doi: 10.1007/BF02295440

McLarenCE, McLachlanGJ, HallidayJW, WebbSI, LeggettBA, JazwinskaEC, Crawford, DHG, GordeukVR, McLarenGD and PowellLW (1998). Distribution of transferrin saturation in an Australian population: Relevance to the early diagnosis of hemochromatosis. Gastroenterology, 114 (3), 543-549. doi: 10.1016/S0016-5085(98)70538-4

McLachlan, G, Welham, J and McGrath, J (1998). Heterogeneity in schizophrenia: A mixture model analysis based on age-of-onset, gender and diagnosis. Schizophrenia Research, 29 (1-2), 25-25. doi: 10.1016/S0920-9964(97)88353-3

Basford, K. E., Mclachlan, G. J. and York, M. G. (1997). Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited. Journal of Applied Statistics, 24 (2), 169-179.

McLachlan, G. J., Ng, S. K., Adams, P., McGiffin, D. C. and Galbraith, A. J. (1997). An algorithm for fitting mixtures of Gompertz distributions to censored survival data. Journal of Statistical Software, 2 (7), 1-23. doi: 10.18637/jss.v002.i07

Basford, K. E., McLachlan, G. J. and York, M. G. (1997). Modelling the distribution of stamp paper thickness via finite normal mixtures: The 1872 Hidalgo stamp issue of Mexico revisited. Journal of Applied Statistics, 24 (2), 169-180. doi: 10.1080/02664769723783

McLachlan, G. J. (1997). On the EM algorithm for overdispersed count data. Statistical Methods in Medical Research, 6 (1), 76-98. doi: 10.1177/096228029700600106

McLachlan, G (1997). On Bayesian analysis of mixtures with an unknown number of components - Discussion. Journal of The Royal Statistical Society Series B-methodological, 59 (4), 758-792.

Basford, K. E., Greenway, D. R., McLachlan, G. J. and Peel, D. (1997). Standard errors of fitted component means of normal mixtures. Computational Statistics, 12 (1), 1-17.

Hawkins, DM and McLachlan, GJ (1997). High-breakdown linear discriminant analysis. Journal of The American Statistical Association, 92 (437), 136-143. doi: 10.2307/2291457

McGiffin, DC, Galbraith, AJ, OBrien, MF, McLachlan, GJ, Naftel, DC, Adams, P, Reddy, S and Early, L (1997). An analysis of valve re-replacement after aortic valve replacement with biologic devices. Journal of Thoracic And Cardiovascular Surgery, 113 (2), 311-318. doi: 10.1016/S0022-5223(97)70328-3

McLachlan, G. (1997). The impact of the EM algorithm on medical statistics. Statistical Methods in Medical Research, 6 (1), 1-2. doi: 10.1191/096228097668772579

McLachlan, GJ, Peel, D and Whiten, WJ (1996). Maximum likelihood clustering via normal mixture models. Signal Processing-Image Communication, 8 (2), 105-111. doi: 10.1016/0923-5965(95)00039-9

McLachlan, G. J. (1996). Likelihood-based approaches to pattern recognition. Far East Journal of Mathematical Sciences, 4 (Pt. 1), 1-29.

McLachlan, GJ and Scot, D (1995). Asymptotic Relative Efficiency of the Linear Discriminant Function Under Partial Nonrandom Classification of the Training Data. Journal of Statistical Computation and Simulation, 52 (4), 415-426. doi: 10.1080/00949659508811689

McLachlan, GJ, McLaren, CE and Matthews, D (1995). An Algorithm for the Likelihood Ratio Test of One Versus 2 Components in a Normal Mixture Model Fitted to Grouped and Truncated Data. Communications in Statistics-Simulation and Computation, 24 (4), 965-985. doi: 10.1080/03610919508813288

Ray, MJ, Hawson, Gat, Just, Sje, McLachlan, G and Obrien, M (1994). Relationship of Platelet-Aggregation to Bleeding After Cardiopulmonary Bypass. Annals of Thoracic Surgery, 57 (4), 981-986.

Shoukri, MM and McLachlan, GJ (1994). Parametric-Estimation in a Genetic Mixture Model with Application to Nuclear Family Data. Biometrics, 50 (1), 128-139. doi: 10.2307/2533203

McLachlan G.J. and McGiffin D.C. (1994). On the role of finite mixture models in survival analysis.. Statistical methods in medical research, 3 (3), 211-226. doi: 10.1177/096228029400300302

Lawoko, Cro and McLachlan, GJ (1994). Estimation of Mixing Proportions in the Presence of Autoregressively Correlated Training Data - the Case of 2 Univariate Normal-Populations. Communications in Statistics-Simulation and Computation, 23 (3), 591-613. doi: 10.1080/03610919408813189

Whittle, P, Kay, J, Hand, DJ, Tarassenko, L, Brown, PJ, Titterington, DM, Taylor, C, Gilks, WR, Critchley, F, Mayne, AJ, Wahba, G, Luttrell, SP, Baczkowski, AJ, Mardia, KV, Breiman, L, Buntine, W, Chatfield, C, Deveaux, RD, Darken, CJ, Ungar, LH, Glendinning, RH, Hastie, T, Tibshirani, R, McLachlan, GJ, Michie, D, Owen, AB, Wolpert, DH and Ripley, BD (1994). Neural Networks and Related Methods for Classification - Discussion. Journal of the Royal Statistical Society Series B-Methodological, 56 (3), 437-456.

Ray, Michael J., Hawson, Geoffrey A.T., Just, Sarah J.E., McLachlan, Geoffery and O'Brien, Mark (1994). Relationship of platelet aggregation to bleeding after cardiopulmonary bypass. The Annals of Thoracic Surgery, 57 (4), 981-986. doi: 10.1016/0003-4975(94)90218-6

McGiffin, DC, Obrien, MF, Galbraith, AJ, McLachlan, GJ, Stafford, EG, Gardner, Mah, Pohlner, PG, Early, L and Kear, L (1993). An Analysis of Risk-Factors for Death and Mode-Specific Death After Aortic-Valve Replacement with Allograft, Xenograft, and Mechanical Valves. Journal of Thoracic and Cardiovascular Surgery, 106 (5), 895-911. doi: 10.1016/s0022-5223(19)34046-2

McLachlan, G (1993). A Connection Between the Logit Model, Normal Discriminant-Analysis, and Multivariate Normal Mixtures - Comment. American Statistician, 47 (1), 88-88.

McGiffin, DC, Galbraith, AJ, McLachlan, GJ, Stower, RE, Wong, ML, Stafford, EG, Gardner, Mah, Pohlner, PG and Obrien, MF (1992). Aortic-Valve Infection - Risk-Factors for Death and Recurrent Endocarditis After Aortic-Valve Replacement. Journal of Thoracic and Cardiovascular Surgery, 104 (2), 511-520. doi: 10.1016/s0022-5223(19)34813-5

Jones, P. N. and McLachlan, G. J. (1992). Fitting finite mixture models in a regression context. Australian Journal of Statistics, 34 (2), 233-240. doi: 10.1111/j.1467-842X.1992.tb01356.x

Jones, Peter N. and McLachlan, Geoffrey J. (1992). Improving the convergence rate of the EM algorithm for a mixture model fitted to grouped truncated data. Journal of Statistical Computation and Simulation, 43 (1-2), 31-44. doi: 10.1080/00949659208811426

Mclachlan G.J. (1992). Cluster analysis and related techniques in medical research. Statistical Methods in Medical Research, 1 (1), 27-48. doi: 10.1177/096228029200100103

McGiffen, David C. and McLachlan, Geoffrey J. (1991). The analysis of time-related events after cardiac surgery. The AustralAsian Journal of Cardiac and Thoracic Surgery, 1 (1), 11-13. doi: 10.1016/1037-2091(91)90007-Y

Jones, PN and McLachlan, GJ (1991). Fitting Mixture Distributions to Phenylthiocarbamide (ptc) Sensitivity. American Journal of Human Genetics, 48 (1), 117-120.

Jones, P. N. and McLachlan, G. J. (1990). Algorithm AS 254: maximum likelihood estimation from grouped and truncated data with finite normal mixture models. Applied Statistics - Journal of the Royal Statistical Society Series C, 39 (2), 273-282. doi: 10.2307/2347776

Jones, P. N. and McLachlan, G. J. (1990). Laplace-normal mixtures fitted to wind shear data. Journal of Applied Statistics, 17 (2), 271-276. doi: 10.1080/757582839

McLachlan, GJ and Gordon, RD (1989). Mixture-Models for Partially Unclassified Data - a Case-Study of Renal Venous Renin in Hypertension. Statistics in Medicine, 8 (10), 1291-1300. doi: 10.1002/sim.4780081012

Jones, PN and McLachlan, GJ (1989). Modeling Mass-Size Particle Data by Finite Mixtures. Communications in Statistics-Theory and Methods, 18 (7), 2629-2646. doi: 10.1080/03610928908830054

Lawoko, Cro and McLachlan, GJ (1989). Bias Associated with the Discriminant-Analysis Approach to the Estimation of Mixing Proportions. Pattern Recognition, 22 (6), 763-766. doi: 10.1016/0031-3203(89)90012-5

McLachlan, GJ and Jones, PN (1988). Fitting Mixture-Models to Grouped and Truncated Data Via the Em Algorithm. Biometrics, 44 (2), 571-578. doi: 10.2307/2531869

Lawoko, Cro and McLachlan, GJ (1988). Further Results On Discrimination with Auto-Correlated Observations. Pattern Recognition, 21 (1), 69-72. doi: 10.1016/0031-3203(88)90073-8

McLachlan, GJ (1988). On the Choice of Starting Values for the Em Algorithm in Fitting Mixture-Models. Statistician, 37 (4-5), 417-425. doi: 10.2307/2348768

Quinn, BG, McLachlan, GJ and Hjort, NL (1987). A Note On the Aitkin-Rubin Approach to Hypothesis-Testing in Mixture-Models. Journal of the Royal Statistical Society Series B-Methodological, 49 (3), 311-314. doi: 10.1111/j.2517-6161.1987.tb01700.x

McLachlan, GJ (1987). On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture. Applied Statistics-Journal of the Royal Statistical Society Series C, 36 (3), 318-324. doi: 10.2307/2347790

McLachlan, GJ (1986). Assessing the Performance of An Allocation Rule. Computers & Mathematics with Applications-Part a, 12 (2), 261-272. doi: 10.1016/0898-1221(86)90079-9

Lawoko, Cro and McLachlan, GJ (1986). Asymptotic Error Rates of the W-Statistics and Z-Statistics When the Training Observations Are Dependent. Pattern Recognition, 19 (6), 467-471. doi: 10.1016/0031-3203(86)90045-2

Basford, KE and McLachlan, GJ (1985). Cluster-Analysis in a Randomized Complete Block Design. Communications in Statistics-Theory and Methods, 14 (2), 451-463. doi: 10.1080/03610928508828924

Basford, K. E. and McLachlan, G. J. (1985). Estimation of allocation rates in a cluster-analysis context. Journal of the American Statistical Association, 80 (390), 286-293. doi: 10.2307/2287884

Lawoko, Cro and McLachlan, GJ (1985). Discrimination with Auto-Correlated Observations. Pattern Recognition, 18 (2), 145-149. doi: 10.1016/0031-3203(85)90038-X

Basford, KE and McLachlan, GJ (1985). The Mixture Method of Clustering Applied to 3-Way Data. Journal of Classification, 2 (1), 109-125. doi: 10.1007/BF01908066

Basford, KE and McLachlan, GJ (1985). Likelihood Estimation with Normal Mixture-Models. Applied Statistics-Journal of the Royal Statistical Society Series C, 34 (3), 282-289. doi: 10.2307/2347474

Do, K and McLachlan, GJ (1984). Estimation of Mixing Proportions - a Case-Study. Applied Statistics-Journal of the Royal Statistical Society Series C, 33 (2), 134-140. doi: 10.2307/2347437

Lawoko, Cro and McLachlan, GJ (1983). Some Asymptotic Results On the Effect of Auto-Correlation On the Error Rates of the Sample Linear Discriminant Function. Pattern Recognition, 16 (1), 119-121. doi: 10.1016/0031-3203(83)90014-6

McLachlan, GJ and Ganesalingam, S (1982). Updating a Discriminant Function On the Basis of Unclassified Data. Communications in Statistics Part B-Simulation and Computation, 11 (6), 753-767. doi: 10.1080/03610918208812293

McLachlan, GJ (1982). On the Bias and Variance of Some Proportion Estimators. Communications in Statistics Part B-Simulation and Computation, 11 (6), 715-726. doi: 10.1080/03610918208812290

McLachlan, GJ, Lawoko, Cro and Ganesalingam, S (1982). On the Likelihood Ratio Test for Compound Distributions for Homogeneity of Mixing Proportions. Technometrics, 24 (4), 331-334. doi: 10.2307/1267829

McLachlan, G. J., Lawoko, C. R O and Ganesalingam, S. (1982). On the likelihood ratio test for compound distributions for homogeneity of mixing proportions. Technometrics, 24 (4), 331-334. doi: 10.1080/00401706.1982.10487796

McLachlan, GJ (1981). Mathematics and Statistics for the Bio-Sciences - Eason,g, Coles,cw, Gettinby,g. Biometrics, 37 (2), 417-417. doi: 10.2307/2530436

Ganesalingam, S and McLachlan, GJ (1981). Some Efficiency Results for the Estimation of the Mixing Proportion in a Mixture of 2 Normal-Distributions. Biometrics, 37 (1), 23-33. doi: 10.2307/2530519

McLachlan G.J. (1980). On the mean square error associated with adaptive generalized ridge regression. Biometrical Journal, 22 (2), 125-129. doi: 10.1002/bimj.4710220205

McLachlan, GJ (1980). The Efficiency of Efrons Bootstrap Approach Applied to Error Rate Estimation in Discriminant-Analysis. Journal of Statistical Computation and Simulation, 11 (3-4), 273-279. doi: 10.1080/00949658008810414

Ganesalingam, S and McLachlan, GJ (1980). A Comparison of the Mixture and Classification Approaches to Cluster-Analysis. Communications in Statistics Part A-Theory and Methods, 9 (9), 923-933. doi: 10.1080/03610928008827932

Ganesalingam, S and McLachlan, GJ (1980). Error Rate Estimation On the Basis of Posterior Probabilities. Pattern Recognition, 12 (6), 405-413. doi: 10.1016/0031-3203(80)90016-3

McLachlan, G (1980). Selection of Variables in Discriminant-Analysis. Biometrics, 36 (3), 554-554.

McLachlan, GJ (1980). On the Relationship Between the F-Test and the Overall Error Rate for Variable Selection in 2-Group Discriminant-Analysis. Biometrics, 36 (3), 501-510. doi: 10.2307/2530218

McLachlan, G. J. and Holt, J. N. (1980). THE COVARIANCE ANALYSIS OF SOME CENSORED SURVIVAL DATA FROM A LARGE SCALE STUDY OF MELANOMA. Australian Journal of Statistics, 22 (3), 237-249. doi: 10.1111/j.1467-842X.1980.tb01173.x

McLachlan, GJ (1980). A Note On Bias Correction in Maximum Likelihood Estimation with Logistic Discrimination. Technometrics, 22 (4), 621-627. doi: 10.2307/1268202

Byth, K. and McLachlan, G. J. (1980). Logicstic regression compared to normal discrimination for non-normal populations. Australian Journal of Statistics, 22 (2), 188-196. doi: 10.1111/j.1467-842X.1980.tb01166.x

Ganesalingam, S. and McLachlan, G. J. (1979). A case study of two clustering methods based on maximum likelihood. Statistica Neerlandica, 33 (2), 81-90. doi: 10.1111/j.1467-9574.1979.tb00665.x

McLachlan, G. J. (1979). Comparison of the Estimative and Predictive Methods of Estimating Posterior Probabilities. Communications in Statistics Part A-Theory and Methods, 8 (9), 919-929. doi: 10.1080/03610927908827807

Ganesalingam, S. and McLachlan, G. J. (1979). Small sample results for a linear discriminant function estimated from a mixture of normal populations. Journal of Statistical Computation and Simulation, 9 (2), 151-158. doi: 10.1080/00949657908810306

McLachlan, G. J. and Byth, K. (1979). Expected error rates for logistic regression versus normal discriminant analysis. Biometrical Journal, 21 (1), 47-56. doi: 10.1002/bimj.4710210107

McLachlan, G. J. (1978). Small sample results for partial classification with the Studentized statistic W. Biometrical Journal, 20 (7-8), 639-644. doi: 10.1002/bimj.197800003

Byth, K and McLachlan, GJ (1978). Biases Associated with Maximum Likelihood Methods of Estimation of Multivariate Logistic Risk Function. Communications in Statistics Part A-Theory and Methods, 7 (9), 877-890. doi: 10.1080/03610927808827679

Ganesalingam, S and McLachlan, GJ (1978). Efficiency of a Linear Discriminant Function Based On Unclassified Initial Samples. Biometrika, 65 (3), 658-662. doi: 10.1093/biomet/65.3.658

McLachlan, G. J. (1977). The bias of sample based posterior probabilities. Biometrical Journal, 19 (6), 421-426. doi: 10.1002/bimj.4710190604

McLachlan, GJ (1977). Constrained Sample Discrimination with Studentized Classification Statistic-W. Communications in Statistics Part A-Theory and Methods, 6 (6), 575-583. doi: 10.1080/03610927708827515

McLachlan, GJ (1977). Note On Choice of a Weighting Function to Give An Efficient Method for Estimating Probability of Misclassification. Pattern Recognition, 9 (3), 147-149. doi: 10.1016/0031-3203(77)90012-7

McLachlan, GJ (1977). Estimating Linear Discriminant Function From Initial Samples Containing a Small Number of Unclassified Observations. Journal of the American Statistical Association, 72 (358), 403-406. doi: 10.2307/2286807

McLachlan, GJ (1976). Further Results On Effect of Intraclass Correlation Among Training Samples in Discriminant-Analysis. Pattern Recognition, 8 (4), 273-275. doi: 10.1016/0031-3203(76)90047-9

McLachlan, GJ (1976). Bias of Apparent Error Rate in Discriminant-Analysis. Biometrika, 63 (2), 239-244. doi: 10.2307/2335615

McLachlan, GJ (1976). Criterion for Selecting Variables for Linear Discriminant Function. Biometrics, 32 (3), 529-534. doi: 10.2307/2529742

McLachlan, GJ (1975). Confidence Intervals for Conditional Probability of Misallocation in Discriminant-Analysis. Biometrics, 31 (1), 161-167. doi: 10.2307/2529717

McLachlan, GJ (1975). Iterative Reclassification Procedure for Constructing An Asymptotically Optimal Rule of Allocation in Discriminant-Analysis. Journal of the American Statistical Association, 70 (350), 365-369. doi: 10.2307/2285824

McLachlan, G. J. (1975). Iterative reclassification procedure for constructing an asymptotically optimal rule of allocation in discriminant analysis. Journal of the American Statistical Association, 70 (350), 365-369. doi: 10.1080/01621459.1975.10479874

McLachlan, GJ (1975). Some Expected Values for Error Rates of Sample Quadratic Discriminant Function. Australian Journal of Statistics, 17 (3), 161-165. doi: 10.1111/j.1467-842x.1975.tb00953.x

McLachla.GJ (1974). Asymptotic Distributions of Conditional Error Rate and Risk in Discriminant-Analysis. Biometrika, 61 (1), 131-135. doi: 10.1093/biomet/61.1.131

McLachla.GJ (1974). Asymptotic Unbiased Technique for Estimating Error Rates in Discriminant-Analysis. Biometrics, 30 (2), 239-249. doi: 10.2307/2529646

McLachla.GJ (1974). Relationship in Terms of Asymptotic Mean-Square Error Between Separate Problems of Estimating Each of 3 Types of Error Rate of Linear Discriminant Function. Technometrics, 16 (4), 569-575. doi: 10.2307/1267609

McLachlan, G. J. (1974). The relationship in terms of asymptotic mean square error between the separate problems of estimating each of the three types of error rate of the linear discriminant function. Technometrics, 16 (4), 569-575. doi: 10.1080/00401706.1974.10489239

McLachla.GJ (1974). Estimation of Errors of Misclassification On Criterion of Asymptotic Mean-Square Error. Technometrics, 16 (2), 255-260. doi: 10.2307/1267948

McLachlan, Geoffrey J. (1973). The errors of allocation and their estimators in the two-population discrimination problem. Bulletin of the Australian Mathematical Society, 9 (01), 149-150. doi: 10.1017/s000497270004301x

McLachla.GJ (1973). Asymptotic Expansion of Expectation of Estimated Error Rate in Discriminant-Analysis. Australian Journal of Statistics, 15 (3), 210-214. doi: 10.1111/j.1467-842X.1973.tb00201.x

McLachla.GJ (1972). Asymptotic Expansion for Variance of Errors of Misclassification of Linear Discriminant Function. Australian Journal of Statistics, 14 (1), 68-72. doi: 10.1111/j.1467-842X.1972.tb00339.x

McLachla.GJ (1972). Asymptotic Results for Discriminant Analysis When Initial Samples Are Misclassified. Technometrics, 14 (2), 415-&. doi: 10.2307/1267432

Zhang, Min, Basford, Kaye, Arief, Vivi, McLachlan, Geoff and Nguyen, Quan (2023). Using cellular composition estimated from bulk RNA-seq data to suggest cellular level markers for melanoma survival. IBS-AR/SEEM Conference, Bay of Islands, Aotearoa, New Zealand, 27 November - 1 December 2023.

Zhang, Min, Arief, Vivi, McLachlan, Geoffrey, Nguyen, Quan and Basford, Kaye (2022). Exploratory data analysis of TCGA skin cutaneous melanoma RNA-seq data. Australasian Applied Statistics Conference (AASC), Inverloch, VIC Australia, 28 November - 2 December 2022.

Chatterjee, Robindra, Valenta, Richard, McLachlan, Geoffrey and Weatherley, Dion (2021). AEGC Machine Learning Workshop presentation. Australian Exploration Geoscience Conference, Online, 14-17 September 2021.

Chatterjee, Robindra , Valenta, Richard , McLachlan, Geoffrey and Weatherley, Dion (2021). Extending FaultSeg3D to Minerals Seismic: Part 1 – A synthetic 3D-seismic training-volume generator for preparing data replicating a hardrock terrane to train an automatic-fault-prediction algorithm. Australian Earth Science Convention, Virtual, 9-12 February 2021.

Lee, Sharon X. and McLachlan, Geoffrey J. (2021). On Mean And/or Variance Mixtures of Normal Distributions. 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), Cassino, Italy, 11–13 September 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69944-4_13

Lee, Sharon X. and McLachlan, Geoffrey J. (2020). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. 20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 , Adelaide, SA, Australia, 1 - 6 December 2013. Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ).

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2019). PPEM: privacy-preserving EM learning for mixture models. 8th International Conference on Applications and Techniques in Information Security, ATIS 2017, Auckland, New Zealand, 6-7 July 2017. Oxford, United Kingdom: John Wiley & Sons. doi: 10.1002/cpe.5208

Jones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2019). Positive data kernel density estimation via the LogKDE package for R. AusDM 2018: 16th Australasian Conference on Data Mining, Bahrurst, NSW, Australia, 28 - 30 November 2018. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-13-6661-1_21

McLachlan, Geoffrey J. and Lee, Sharon X. (2019). Flexible modelling via multivariate skew distributions. Research School on Statistics and Data Science (RSSDS 2019), Melbourne, VIC, Australia, 24–26 July 2019. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-15-1960-4_4

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Privacy distributed three-party learning of Gaussian mixture models. International Conference on Applications and Technologies in Information Security (ATIS), Auckland, New Zealand, 6-7 July 2017. Singapore: Springer Singapore. doi: 10.1007/978-981-10-5421-1_7

Leemaqz, Kaleb L., Lee, Sharon X. and McLachlan, Geoffrey J. (2017). Corruption-resistant privacy preserving distributed EM algorithm for model-based clustering. 2017 IEEE Trustcom/BigDataSE/ICESS, Sydney, NSW, Australia, 1 - 4 August 2017. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/Trustcom/BigDataSE/ICESS.2017.356

Ng, Shu Kay and McLachlan, Geoffrey J. (2017). On the identification of correlated differential features for supervised classification of high-dimensional data. 15th Conference of the International Federation of Classification Societies (IFCS), Bologna, Italy, July 5-8, 2015. Cham, Switzerland: Springer. doi: 10.1007/978-3-319-55723-6_4

Nguyen, Hien D. and McLachlan, Geoffrey J. (2017). Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approach. Future Technologies Conference (FTC) 2017, Vancouver, Canada, 29-30 November 2017. Piscataway, NJ United States: IEEE.

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). On mixture modelling with multivariate skew distributions. COMPSTAT: International Conference on Computational Statistics, Oviedo, Spain, 23-26 August 2016. The Hague, The Netherlands: The International Statistical Institute/International Association for Statistical Computing.

Lee, Sharon X. and McLachlan, Geoffrey J. (2016). Unsupervised component-wise EM learning for finite mixtures of skew t-distributions. 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, 12-15 December 2016. New York, NY, United States: Springer. doi: 10.1007/978-3-319-49586-6_49

Ng, Shu-Kay and McLachlan, Geoffrey J. (2016). Finding group structures in "Big Data" in healthcare research using mixture models. IEEE International Conference on Bioinformatics and Biomedicine, Shenzhen, China, 15-18 December 2016. Piscataway, NJ, United States: IEE Computer Society. doi: 10.1109/BIBM.2016.7822692

Lee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2016). A simple parallel EM algorithm for statistical learning via mixture models. International Conference on Digital Image Computing, Gold Coast, QLD, Australia, 30 November - 2 December,2016. Piscataway, NJ, United States: IEEE (Institute for Electrical and Electronic Engineers). doi: 10.1109/DICTA.2016.7796997

García-Escudero, L. A., Greselin, F., Mayo-Iscar, A. and McLachlan, G. J. (2016). Robust estimation of mixtures of skew-normal distributions. Scientific Meeting of the Italian Statistical Society, Salerno, Italy, 8-10 November 2016. Fisciano, Italy: Dipartimento di Scienze Economiche e Statistiche, University of Salerno..

Ng, Shu-Kay and McLachlan, Geoffrey J. (2014). Mixture of regression models with latent variables and sparse coefficient parameters. COMPSTAT 2014, Geneva Switzerland, 19- 22 August 2014. Hague, Netherlands: The International Statistical Institute/International Association for Statistical Computing.

Tian, T., McLachlan, G., Dieters, M. and Basford, K. (2014). Application of multiple imputation to incomplete three-way three-mode multi-environment trial data. International Biometric Conference, Florence (Italy), 6-11 July 2014. Florence, Italy: International Biometric Society.

Nguyen, Hien D. and McLachlan, Geoffrey J. (2014). Asymptotic inference for hidden process regression models. 2014 IEEE Workshop on Statistical Signal Processing (SSP 2014), Gold Coast, Australia, 29 June - 2 July 2014. Piscataway, NJ, United States: IEEE. doi: 10.1109/SSP.2014.6884624

McLachlan, Geoff (2014). Making sense of a random world through statistics. AusDM 2014, Brisbane, QLD, Australia, 27-28 November 2014. Darlinghurst, NSW, Australia: Australian Computer Society.

McLachlan, Geoffrey J. and Leemaqz, Sharon X. (2013). On finite mixtures of skew distributions. 28th International Workshop on Statistical Modelling, Palermo, Italy, 8-12 July 2013. Amsterdam: Statistical Modelling Society.

Tian, Ting, McLachlan, Geoff, Dieters, Mark and Basford, Kaye (2013). Evaluating methods of estimating missing values for three-way three-mode multi-environment trial data. Biometrics by the Canals: The International Biometric Society Australasian Region Conference 2013, Mandura, WA, Australia, 1-5 December, 2013.

Nguyen, Hien D., McLachlan, Geoffrey J., Janke, Andrew L., Cherbuin, Nicolas, Sachdev, Perminder and Anstey, Kaarin J. (2013). Spatial false discovery rate control for magnetic resonance imaging studies. International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013, Hobart, TAS, 26 - 28 November 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/DICTA.2013.6691531

Sun, Mingzhu and McLachlan, Geoffrey J (2013). A common factor-analytic model for classification. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai China, 18 - 21 December 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/BIBM.2013.6732722

Ng, Shu-Kay and McLachlan, Geoffrey J. (2013). Using cluster analysis to improve gene selection in the formation of discriminant rules for the prediction of disease outcomes. IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai, China, 18 - 21 December 2013. Piscataway, NJ United States: I E E E. doi: 10.1109/BIBM.2013.6732501

Kim, Sunghoon, Li, Guo-Zheng, Ressom, Habtom, Hughes, Michael, Liu, Baoyan, McLachlan, Geoff, Liebman, Michael, Sun, Hongye and Hu, Xiaohua (2013). Preface. 2013 IEEE International Conference on Bioinformatics and Biomedicine, Shanghai, China, 18-21 December 2013. Minerals, Metals and Materials Society. doi: 10.1109/BIBM.2013.6732445

Lee, Sharon X. and McLachlan, Geoffrey J. (2013). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. International Congress on Modelling and Simulation, Adelaide, SA, Australia, 1/12/2013/6/12/2013. Melbourne, Australia: Modelling and Simulation Society of Australia and New Zealand.

Wojnarski, Marcin, Janusz, Andrzej, Nyugen, Hung Son, Bazan, Jan, Luo, ChuanJiang, Chen, Ze, Hu, Feng, Wang, Guoyin, Guan, Lihe, Luo, Huan, Gao, Juan, Shen, Yuanxia, Nikulin, Vladimir, Huang, Tian-Hsiang, McLachlan, Geoffrey J., Bosnjak, Matko and Gamberger, Dragan (2010). RSCTC 2010 Discovery Challenge: Mining DNA microarray data for medical diagnosis and treatment. 7th International Conference on Rough Sets and Current Trends in Computing (RSCTC 2010), Warsaw, Poland, 28-30 June 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-13529-3_3

Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Identifying fibre bundles with regularized k-means clustering applied to grid-based data. 2010 International Joint Conference on Neural Networks (IJCNN 2010), Barcelona, Spain, 18-23 July 2010. United States: IEEE Computer Society. doi: 10.1109/IJCNN.2010.5596562

Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On the gradient-based algorithm for matrix factorization applied to dimensionality reduction. BIOINFORMATICS 2010: 1st International Conference on Bioinformatics, Valencia, Spain, 20-23 January 2010. Portugal: Institute for Systems and Technologies of Information, Control and Communication.

Nikulin, Vladimir, Huang, Tian-Hsiang and McLachlan, Geoffrey J. (2010). A comparative study of two matrix factorization methods applied to the classification of gene expression rate. IEEE International Conference on Bioinformatics & Biomedicine, Hong Kong, 18-21 December 2010. Los Alamitos, CA, U.S.A.: IEEE Computer Society. doi: 10.1109/bibm.2010.5706640

Huang, Tian-Hsiang, Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). On relations between genes and metagenes obtained via gradient-based matrix factorization. 2010 IEEE/ICME International Conference on Complex Medical Engineering, Gold Coast, Australia, 13-15 July 2010. Piscataway, United States: IEEE Computer Society. doi: 10.1109/ICCME.2010.5558880

Pyne, Saumyadipta, Hu, Xinli, Wang, Kui, Rossin, Elizabeth, Lin, Tsung-I, Maier, Lisa, Baecher-Allan, Clare, McLachlan, Geoffrey, Tamayo, Pablo, Hafler, David, De Jager, Philip and Mesirov, Jill (2010). Automated high-dimensional flow cytometric data analysis. 14th Annual International Conference on Research in Computational Molecular Biology, Lisbon, Portugal, 25-28 April 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-12683-3_41

Nikulin, Vladimir and McLachlan, Geoffrey J. (2010). Penalized principal component analysis of microarray data. 6th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2009, Genoa, Italy, 15-17 October, 2009. Germany: Springer. doi: 10.1007/978-3-642-14571-1_7

McLachlan, Geoff J. and Baek, Jangsun (2010). Clustering of High-Dimensional Data via Finite Mixture Models. 32nd Annual Conference of the German-Classification-Society, Hamburg Germany, Jul 16-18, 2008. BERLIN: SPRINGER-VERLAG BERLIN. doi: 10.1007/978-3-642-01044-6_3

McLachlan, Geoffrey J. and Wockner, Leesa (2010). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. Classification as a Tool for Research (GfKl 2009), Dresden, Germany, 13-18 March 2009. doi: 10.1007/978-3-642-10745-0-18

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. AISTATS 2009, Clearwater Beach, FL, United States, 16-18 April 2009. Cambridge, MA, United States: M I T Press.

Wang, Kui, Ng, Shu-Kay and McLachlan, Geoffrey J. (2009). Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. 2009 Conference of Digital Image Computing: Techniques and Applications, Melbourne, Australia, 1-3 December 2009. Los Alamitos, California: IEEE Computer Society. doi: 10.1109/DICTA.2009.88

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). On a general method for matrix factorisation applied to supervised classification. 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, Washington, D.C., U.S.A., 1-4 November 2009. Piscataway, NJ, United States: IEEE. doi: 10.1109/BIBMW.2009.5332135

Nikulin, Vladimir, McLachlan, Geoffrey J. and Ng, Shu Kay (2009). Ensemble approach for the classification of imbalanced data. AI 2009: Advances in Artificial Intelligence, Melbourne, VIC, Australia, 1-4 December 2009. Berlin, Germany: Springer. doi: 10.1007/978-3-642-10439-8_30

Nikulin, Vladimir and McLachlan, Geoffrey J. (2009). Regularised k-means clustering for dimension reduction applied to supervised classification. Sixth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics 2009, Genova, Italy, 15-17 October 2009. Salerno, Italy: DMI Proceedings Series.

McLachlan, G. J., Ng, S. K. and Wang, K. (2008). Clustering via mixture regression models with random effects. 18th Symposium on Computational Statistics (COMSTAT 2008), Porto, Portugal, 24-29 August 2008. Heidelberg, Germany: Physica-Verlag,. doi: 10.1007/978-3-7908-2084-3_33

Lenzenweger, M. F., McLachlan, G. and Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using em-based finite mixture modeling. 10th International Congress on Schizophrenia Research, Savannah Ga, 02-06 April 2005. Oxford, United Kingdom: Oxford University Press. doi: 10.1093/schbul/sbm004

McLaren, C. E., Gordeuk, V. R., Chen, W. P., Barton, J. C., Acton, R. T., Speechley, M., Castro, O., Adams, P. C., Snively, B. M., Harris, E. L., Reboussin, D. M., McLachlan, G. J., Bean, R. and McLaren, G. D. (2007). Subpopulations with iron deficiency, liver disease, or HFE mutations revealed by statistical mixture modeling of transferrin saturation and serum ferritin concentration in Asians, African American, Hispanics, and Whites. 49th Annual Meeting of the American Society of Hematology, Atlanta, GA, U.S.A., 8 - 11 December 2007. Washington, DC, U.S.A.: American Society of Hematology.

Nikulin, V and McLachlan, GJ (2007). Merging algorithm to reduce dimensionality in application to web-mining. 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Qld, Australia, 2-6 December, 2007. Berlin, Germany: Springer Berlin / Heidelberg. doi: 10.1007/978-3-540-76928-6_88

Ng, S. K., McLachlan, G. J., Wang, K., Jones, L. Ben-Tovim and Ng, S. W. (2006). A mixture model with random-effects components for clustering correlated gene-expression profiles. doi: 10.1093/bioinformatics/btl165

Ng, Shu-Kay, Wang, Kui and McLachlan, Geoffrey J. (2006). Multilevel modelling for inference of genetic regulatory networks. Complex Systems, Brisbane, Australia, 11-14 December 2005. Bellingham, WA, United States: SPIE - International Society for Optical Engineering. doi: 10.1117/12.638449

Ng, S K, McLachlan, G J, Bean, R W and NG, SW (2006). Clustering replicated microarray data in mixtures of random effects models for varius covariance structures. 2006 Workshop on Intelligent Systems for Bioinformatics (WISB, Hobart, Australia, 4 December 2006. Sydney: The Australian Computer Society.

Basford, Kaye, McLachlan, Geoff and Bean, Richard (2006). Issues of robustness and high dimensionality in cluster analysis. 17th Symposium on Computational Statistics (COMSTAT 2006), Rome, Italy, 28 August - 1 September 2006. Rome, Italy: Physica-Verlag. doi: 10.1007/978-3-7908-1709-6_1

Ng, A.S.K. and McLachlan, G. J. (2005). Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal Data. WDIC2005, Griffith University, 21 February 2005. Brisbane, Australia: Australian Pattern Recognition Society.

Ng, A. S. K. and McLachlan, G. J. (2005). Normalized Gaussian Networks with Mixed Feature Data. 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, 5-9 Dec 2005. Berlin, Germany: Springer-Verlag. doi: 10.1007/11589990_101

Jones, LBT, Bean, R, McLachlan, G and Zhu, J (2005). Application of mixture models to detect differentially expressed genes. Berlin: Springer-Verlag Berlin. doi: 10.1007/11508069_55

McLachlan, G. J., Chang, S., Mar, J. and Ambroise, C. (2004). On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples. Second Asia-Pacific Bioinformatics Conference, Dunedin, New Zealand, 18-22 January 2004. Sydney, Australia: Australian Computer Society.

Jones, L., Ng, A.S. K., Monico, K. A. and McLachlan, G. J. (2004). Linking gene-expression experiments with survival-time data. 19th International Workshop on Statistical Modelling, Florence, 4-8 July 2004. Italy: Firenze University Press.

Kim, S-G., Ng, A.S. K., McLachlan, G. J. and Wang, D. (2003). Segmentation of brain MR images with bias field correction. WDIC 2003, The University of Queensland, Brisbane, 7 February 2003. Brisbane, Australia: The University of Queensland.

Ng, A. S. K. and McLachlan, G. J. (2003). Robust estimation in Gaussian mixtures using multiresolution Kd -trees. Seventh International Conference on Digital Image Computing: Techniques and Applications, DICTA 2003, Sydney, Australia, 10-12 December 2003. Melbourne, Australia: CSIRO Publishing.

McLachlan, GJ, Ng, SK and Peel, D (2003). On clustering by mixture models. 25th Annual Conference of the German-Classification-Society, Munich Germany, Mar 14-16, 2001. BERLIN: SPRINGER-VERLAG BERLIN.

Ng, A.S. K. and McLachlan, G. J. (2002). On speeding up the EM algorithm in pattern recognition: A comparison of incremental and multiresolution KD -tree-based approaches. Proc. of the Sixth Digital Image Computing Techniques & Applications, Melbourne University, 21-22 January. Melbourne: Australian Pattern Recognition Society.

McLaren, C. E., Cadez, I. V., Smyth, P. and McLachlan, G. J. (2000). Multivariate mixture models for classification of anemias. 2000 Proceedings of the Biometrics Sect. of the Amer.Stat.Ass, Indianapolis, USA, August 2000. Virginia, USA: American Statistical Association.

McLachlan, G. J. and Peel, D. (2000). Mixture of factor analyzers. Seventh International Conference on Machine Learning (ICML - 2000), California, United States, June 29 - July 2 2000. San Francisco, CA United States: Morgan Kaufmann.

Greenway, D. R., Peel, D., Basford, K. E. and McLachlan, G. J. (1999). Extending the two-way mixture model program EMMIX to analyse incomplete data. Biometrics 99, Univ. of Tas., Hobart, Tas, Aust., 12-16 December 1999. Hobart, Aust.: Int. Biomtric Society, Australasian Region.

Cadez, I. V., McLaren, C. E., Smyth, P. and Mclachlan, G. J. (1999). Hierarchical models for the screening of iron deficiency anemia. Sixteenth International Conference on Machine Learning (ICML-99), Bled, Slovenia, June 27-30, 1999. Los Gatos, California: Morgan Kaufmann.

Mclachlan, G. J. and Peel, D. (1999). Computing issues for the EM algorithm in mixture models. Interface '99, Schaumbury, Illinois, June 1999. Fairfax Station, Virginia: Interface Foundation of North America.

McLachlan G.J. and Peel D. (1998). Robust cluster analysis via mixtures of multivariate t-distributions. 7th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition, SSPR 1998 and 2nd International Workshop on Statistical Techniques in Pattern Recognition, SPR 1998, August 11, 1998-August 13, 1998. Springer Verlag. doi: 10.1007/bfb0033290

McLachlan, GJ and Peel, D (1998). MIXFIT: An algorithm for the automatic fitting and testing of normal mixture models. 14th International Conference on Pattern Recognition, Brisbane Australia, Aug 16-20, 1998. LOS ALAMITOS: IEEE COMPUTER SOC. doi: 10.1109/icpr.1998.711203

Feelders, A. J., Chang, Soong and McLachlan, G. J. (1998). Mining in the presence of selectivity bias and its application to reject inference. 4th International Conference on Knowledge Discovery and Data Mining, New York, United States, 27-31 August 1998. AAAI Press.

McLachlan, GJ, Ng, SK, Galloway, GJ and Wang, D (1996). Clustering of magnetic resonance images. Symposium of the Statistical-Computing-Section, at the Annual Meeting of the American-Statistical-Association, Chicago Il, Aug 04-08, 1996. ALEXANDRIA: AMER STATISTICAL ASSOC.

McLaren, C.E., McLachlan, G.J., Webb, S.J., Jazwinska, E.C., Crawford, D.H.G., Gordeuk, V.R., McLaren, G.D. and Powell, LW (1995). The distribution of transferrin saturation in an asymptomatic Australian population: relevance to the early diagnosis of hemochromatosis Washington, from. December 1-5, 1995.. 37th Annual Meeting of the American Society of Hematology, Seattle, WA, United States, 1-5 December 1995. Washington, DC, United States: American Society of Hematology.

McLaren, CE, McLaren, GD, Kambour, EL, McLachlan, GJ, Lukaski, HC, Li, X and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. PHILADELPHIA: SLACK INC.

McLaren, GD, McLaren, CE, Kambour, EL, Lukaski, HC, Xia, L, McLachlan, GJ and Brittenham, GM (1994). Early Detection of the Development of Iron-Deficiency by Patient-Specific Sequential-Analysis of Hematological Tests. THOROFARE: W B SAUNDERS CO.

O'Brien, M. F., McGiffin, D. C., Stafford, E. G., Gardner, M. A.H., Pohlner, P. F., McLachlan, G. J., Gall, K., Smith, S. and Murphy, E. (1991). Allograft aortic valve replacement: Long-term comparative clinical analysis of the viable cryopreserved and antibiotic 4 °C stored valves. V International Symposium on Cardiac Bioprostheses, Avignon, France, 24–27 May 1991. Hoboken, NJ United States: Wiley-Blackwell. doi: 10.1111/jocs.1991.6.4s.534

Holt, JN and McLachlan, GJ (1979). Analysis of Some Censored Survival Data From a Large-Scale Study of Melanoma. WASHINGTON: INTERNATIONAL BIOMETRIC SOC.

McLachlan, GJ (1978). Bias Associated with Maximum Likelihood Estimation of Multivariate Logistic Risk Function. WASHINGTON: INTERNATIONAL BIOMETRIC SOC.

Ahfock, Daniel, McLachlan, Geoffrey, Yang, Liu and Zhu, Min (2022). Detecting accounting fraud with noisy labels. UQ Business School.

McLachlan, G. J. (2001). Multivariate analysis: Classification and discriminant analysis.

Professor Geoffrey McLachlan

Researcher biography

Areas of research

Professor Geoffrey McLachlan

Researcher biography

Publications

Books

Book Chapters

Journal Articles

Conference Papers

Working Paper

Reference Entry

Areas of research