PUBLICATIONS

BOOKS

EM 2nd Edition Book Picture The EM Algorithm and Extensions. Second Edition.
by G.J. McLachlan and T. Krishnan AMGED Book Picture Analyzing Microarray Gene Expression Data
by G.J. McLachlan, K.-A. Do, and C. Ambroise FMM Book Picture Finite Mixture Models
by G.J. McLachlan and D. Peel EM Book Picture The EM Algorithm and Extensions
by G.J. McLachlan and T. Krishnan DASPAR Book Picture Discriminant Analysis and Statistical Pattern Recognition
by G.J. McLachlan Mixture Models: Inference and Applications to Clustering
by G.J. McLachlan and K.E. Basford

PUBLICATIONS (in categories)

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 Mesirov, J.P. (2009). Automated high-dimensional flow cytometric data analysis. Proceedings of the National Academy of Sciences USA 106, 8519-8524. [See commentary: Peng, W. (2009). Going with the skewed flow. Nature Methods 6, 482-483.]

Baek, J., McLachlan, G.J., and Flack, L. (2009). Mixtures of factor analyzers with common factor loadings: applications to the clustering and visualisation of high-dimensional data. IEEE Transactions on Pattern Analysis and Machine Intelligence. To appear.

Flack, L.K. and McLachlan, G.J. (2009). Clustering methods for gene-expression data. In Handbook of Research on Systems Biology Applications in Medicine, A. Daskalaki (Ed.). Hershey, Pennsylvania: Idea Group Publishing, pp. 209-220.

Le Cao, K.-A. and McLachlan, G.J. (2009). Statistical analysis of microarray data: selection of gene prognosis signature. In Computational Biology: Issues and Applications in Oncology, T. Pham (Ed.). New York: Springer-Verlag. To appear.

McLachlan, G.J. (2009). Unsupervised data mining: statistical model-based clustering. In Comprehensive Chemometrics: Chemical and Biochemical Data Analysis Vol. 2, S. Brown, R. Tauler, and R. Walczak (Eds.). Oxford: Elsevier, pp. 655-681.

McLachlan, G.J. and Baek, J. (2009). Clustering of high-dimensional data via finite mixture models. In Advances in Data Analysis, Data Handling and Business Intelligence, A. Fink, B. Lausen, W.Seidel, and A. Ultsch (Eds.). Berlin: Springer-Verlag. To appear.

McLachlan, G.J., Baek, J., and Rathnayake, S. (2009). Mixtures of factor analyzers for the clustering and visualisation of high-dimensional data. In Advances in Latent Class Analysis: A Festschrift in Honour of Professor C. Mitchell Dayton, G. Hancock and G. Macready (Eds.). To appear.

McLachlan, G.J., Flack, L., Ng, S.K., and Wang, K. (2009). Clustering of gene-expression data via normal mixture models. In Statistical Methods for Microarray Data, A.Y. Yakovlev, L. Klebanov, and D. Gaile (Eds.). Totowa, New Jersey: Humana Press. To appear.

McLachlan, G.J. and Ng, S.K. (2009). The EM Algorithm. In The Top-Ten Algorithms in Data Mining, X. Wu and V. Kumar (Eds.). Boca Raton, Florida: Chapman & Hall/CRC, pp. 93-115.

McLachlan, G.J., Ng, S.K., and Wang, K. (2009). Clustering of high-dimensional and correlated data. In Data Analysis and Classification: from the Exploratory to the Confirmatory Approach, C. Lauro, F. Palumbo, and M. Greenacre (Eds.). Berlin: Springer-Verlag. To appear.

McLachlan, G.J. and Wockner, L. (2009). Use of mixture models in multiple hypothesis testing with applications in bioinformatics. In Studies in Classification, Data Analysis, and Knowledge Organization: Classification as a Tool for Research, H. Locarek-Junge and C. Weihs (Eds.). Berlin: Springer. To appear.

Ng, S.K. and McLachlan, G.J. (2009). Expert networks with mixed continuous and categorical feature variables: a location modeling approach. In Machine Learning Research Progress, H. Peters and M. Vogel (Eds.). Hauppauge, New York: Novinka Books, pp. 1-14.

Nikulin, V., McLachlan, G.J., and Ng, S.K. (2009). Ensemble approach for the classification of imbalanced data. Lecture Notes in Artificial Intelligence 5866, 291-300.

Nikulin, V. and McLachlan, G.J. (2009). Classification of imbalanced marketing data with balanced random sets. Journal of Machine Learning Research: Workshop and Conference Proceedings 7, 89-100.

Nikulin, V. and McLachlan, G.J. (2009). Regularised k-means clustering for dimension reduction applied to supervised classification. Lecture Notes in Bioinformatics. To appear.

Nikulin, V. and McLachlan, G.J. (2009). On a general method for matrix factorisation applied to supervised classification. In Proceedings of 2009 IEEE International Conference on Bioinformatics and Biomedicine Workhops, Washington, D.C., J. Chen et al. (Eds.). Piscataway, New Jersey: IEEE, pp. 44-49.

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

Wang, K., Ng, S.K., and McLachlan, G.J. (2009). Multivariate skew t mixture models: applications to fluorescence-activated cell sorting data. Proceedings of DICTA 2009, Conference of Digital Image Computing: Techniques and Applications, Melbourne. To appear.

Zhao, Y., Lee, A.H., Yau, K.K.W., Burke, V., and McLachlan, G.J. (2009). A score test for assessing the cured proportion in the long-term survivor mixture model. Statistics in Medicine 28, 3454-3466.

Zhao, Y., Lee, A.H., Yau, K.K.W., and McLachlan, G.J. (2009). Assessing the adequacy of Weibull survival mixture models: a simulated envelope approach. Journal of Statistical Computation and Simulation.

Le Cao, K.-A., Meugnier, E., and McLachlan, G.J. (2009). The combination of clinical and genetic markers can improve cancer prognosis.

Ng, S.K., McLachlan, G.J., Wang, K., Wockner, L., Zoltan, N., and Ng, S.W. (2009). A random-effects mixture model for ranking correlated differentially-expressed genes.

Nikulin V. and McLachlan, G.J. (2009). Penalized principal component analysis of microarray data.

Nikulin V. and McLachlan, G.J. (2009). On a gradient-based algorithm for matrix factorization applied to dimensionality reduction.

Wang, K., Hu, X., Maier, L.M., Nazaire, M.-D., Tamayo, P., De Jager, P.L., Ng, S.K., Hafler, D.A., Pyne, S., Mesirov, J.P., and McLachlan, G.J. (2009). Joint modeling and registration of cell populations in a cohort of high-dimensional flow cytometric samples.

Wockner, L., McLachlan, G.J., Ng, S.K., and Wood, I. (2009). Detection of differential expression via clustering of gene profiles.

Baek, J. and McLachlan, G.J. (2008). Mixtures of factor analyzers with common factor loadings for the clustering and visualisation of high-dimensional data. Technical Report NI08018-SCH, Preprint Series of the Isaac Newton Institute for Mathematical Sciences, Cambridge.

Caprania, C.C., O'Brien, E., and McLachlan, G.J. (2008). Characteristic traffic load effects from a mixture of loading events on short to medium span bridges. Structural Safety 30, 394-404.

Jorgensen, M.A. and McLachlan, G.J. (2008). Wallace's approach to unsupervised learning: the Snob program. Computer Journal 51, 571-578.

McLachlan, G.J., Bean, R., and Ng, S.K. (2008). Clustering of microarray data via mixture models. In Statistical Advances in Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics, A. Biswas, S. Datta, J.P. Fine, and M.R. Segal (Eds.). Hoboken, New Jersey: Wiley, pp. 365-384.

McLachlan, G.J., Bean, R., and Ng, S.K. (2008). Clustering. In Bioinformatics, Vol. 2: Structure, Function, and Applications, J.M. Keith (Ed.). Totowa, New Jersey: Humana Press, pp. 423-439.

McLachlan, G.J., Chevelu, J., and Zhu, J. (2008). Correcting for selection bias via cross-validation in the classification of microarray data. In Beyond Parametrics in Interdisciplinary Research: Festschrift in Honour of Professor Pranab K. Sen, N. Balakrishnan, E. Pena, and M.J. Silvapulle (Eds.). Hayward, California: IMS Collections, Vol. 1, pp. 383-395.

McLachlan, G.J. and Krishnan, T. (2008). The EM Algorithm and Extensions. Second Edition. Hoboken, New Jersey: Wiley.

McLachlan,G.J., Ng, S.K., and Wang, K. (2008). Clustering via mixture regression models with random effects. In Proceedings of COMPSTAT 2008, P. Brito (Ed.). Heidelberg: Springer, 397-407.

McLachlan, G.J., Wang, K., and Ng, S.K. (2008). Large-scale simultaneous inference with applications to the detection of differential expression with microarray data (with discussion). Statistica 68, 1-30.

McLachlan.G.J., Wang, K., and Ng, S.K. (2008). Contribution to the discussion of the paper by F. Chiaromonte and S. Tyekucheva. Test 17, 43-46.

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.M., 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, 97-109.

Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, S.K., 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, 1-37.

Zhu, J.X., McLachlan, G.J., Ben-Tovim, L., and Wood, I. (2008). On selection biases with prediction rules formed from gene expression data. Journal of Statistical Planning and Inference 38, 374-386.

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, 458-465.

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 2, 1-19.

Lee, A.H., Wang, K., Yau, K.K.W., McLachlan, G.J., and Ng, S.K. (2007). Maternity length of stay modelling by gamma mixture regression with random effects. Biometrical Journal 49, 750-764.

Lenzenweger, M.F., McLachlan, G.J., and Rubin, D.B. (2007). Resolving the latent structure of schizophrenia endophenotypes using EM-based finite mixture modeling. Journal of Abnormal Psychology 116, 16-29.

McLachlan, G.J. (2007). Discriminant analysis. In The Encyclopedia of Measurement and Statistics Vol. 1, N.J. Salkind (Ed.). Thousand Oaks, California: Sage, pp. 267-270.

McLachlan, G.J., Bean, R.W., and Ben-Tovim Jones, L. (2007). Extension of the mixture of factor analyzers model to incorporate the multivariate t distribution. Computational Statistics and Data Analysis 51, 5327-5338.

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, 57-67.

Nikulin, V. and McLachlan, G.J. (2007). Merging algorithm to reduce dimensionality in application to web-mining. Lecture Notes in Artificial Intelligence 4830, 755-761.

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 Journal of Health Science 26, 401-421.

Wang, K., Yau, K.K.W., Lee, A.H., and McLachlan, G.J. (2007). Two-component Poisson mixture regression modelling of count data with bivariate random effects. Mathematical and Computer Modelling 46, 1468-1476.

Wang, K., Yau, K.K.W., Lee, A.H., and McLachlan, G.J. (2007). Multilevel survival modelling of recurrent urinary tract infections. Computer Methods and Programs in Biomedicine 87, 225-229.

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, 1608-1622.

Basford, K.E., McLachlan, G.J., and Bean, R.W. (2006). Issues of robustness and high dimensionality in cluster analysis. In Proceedings of COMPSTAT 2006, A. Rizzi and M. Vichi (Eds.). New York: Springer, pp. 3-15.

Ben-Tovim Jones, L., Bean, R.W., McLachlan, G.J., and Zhu, J.X. (2006). Mixture models for detecting differentially expressed genes in microarrays. International Journal of Neural Systems 16, 353-362.

Lee, A.H., Wang, K., Scott, J.A., Yau, K.K.W., and McLachlan, G.J. (2006). Multilevel zero-inflated Poisson regression modelling of correlated count data with excess zeros. Statistical Methods in Medical Research 15, 47-61.

McLachlan, G.J., Bean, R.W., and Ben-Tovim Jones, L. (2006). A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays. Bioinformatics 22, 1608-1615.

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

Ng, S.K., McLachlan, G.J., Bean, R.W., and Ng, S.W. (2006). Clustering replicated microarray data via mixtures of random effects models for various covariance structures. In Conferences in Research and Practice in Information Technology, Vol. , M. Boden and T.L. Bailey (Eds.). Sydney: The Australian Computer Society, pp. 29-33.

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, 257-267.

Ng, S.K., McLachlan, G.J., Wang, K., Ben-Tovim, L., and Ng, S.W. (2006). A mixture model with random-effects components for clustering correlated gene-expression profiles. Bioinformatics 22, 1745-1752.

Ng, S.K., Wang, K., and McLachlan, G.J. (2006). Multilevel modelling for inference of genetic regulatory networks. In Proceedings of SPIE 2005, Complex Systems in the International Symposium on Microelectronics, MEMS, and Nanotechnology, Vol. 6039, A. Bender (Ed.). Bellingham, Washington: International Society for Optical Engineering, pp. 60390S-1 - 60390S-12.

Xiang, L., Lee, A.H., Yau, K.K.W., and McLachlan, G.J. (2006). A score test for zero-inflation in correlated count data. Statistics in Medicine 25, 1660-1670.

Zhu, X., Ambroise, C., and McLachlan, G.J. (2006). Selection bias in working with the top genes in supervised classification of tissue samples. Statistical Methodology 3, 29-41.

Bean, R.W. and McLachlan, G.J. (2005). Cluster analysis of high-dimensional data: a case study. Lecture Notes in Computer Science 3578, 302-310.

Ben-Tovim Jones, L., Bean, R.W., McLachlan, G.J., and Zhu, J. (2005). Application of mixture models to detect differentially expressed genes. Lecture Notes in Computer Science 3578, 422-431.

Ben-Tovim Jones, L., Ng, S.K., Ambroise, C., Monico, K., 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. In Methods of Microarray Data Analysis IV, J.S. Shoemaker and S.M. Lin (Eds.). New York: Springer, pp. 163-173.

Kerr, R.J., McLachlan, G.J., and Henshall, J.M. (2005). Use of the EM algorithm to detect QTL affecting multiple-traits in a n across half-sib family analysis. Genetics Selection Evolution 37, 83-103.

McLachlan, G.J., Bean, R.W., Ben-Tovim Jones, L., and Zhu, X. (2005). Using mixture models to detect differentially expressed genes. Australian Journal of Experimental Agriculture 45, 859-866.

Ng, S.K. and McLachlan, G.J. (2005). Mixture model-based statistical pattern recognition of clustered or longitudinal data. In Proceedings of WDIC2005, APRS Workshop on Digital Image Computing, B.C. Lovell and A. Maeder (Eds.). Brisbane: Australian Pattern Recognition Society, pp. 139-144.

Ng, S.K. and McLachlan, G.J. (2005). Normalized Gaussian networks with mixed feature data. Lecture Notes in Artificial Intelligence 3809, 879-882.

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

Ben-Tovim Jones, L., Ng, S.K., Monico, K., and McLachlan, G.J. (2004). Linking gene-expression experiments with survival-time data. In Statistical Modelling, Proceedings of the 19th International Workshop on Statistical Modelling. A. Biggeri, E. Dreassi, C. Lagazio, and M. Marchi (Eds.). Florence: Firenze University Press, pp. 71-75.

McLachlan, G.J. and Bean, R. (2004). Contribution to the discussion of paper by J. Friedman and J. Meulman. Journal of the Royal Statistical Society B 66, 846.

McLachlan, G.J. and Chang, S.U. (2004). Mixture modelling for cluster analysis. Statistical Methods in Medical Research 13, 347-361.

McLachlan, G.J. 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, 90-105.

McLachlan, G.J., Chang, S.U., Mar, J., and Ambroise, C. (2004). On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samples. In Conferences in Research and Practice in Information Technology Vol. 29, Y.-P. Chen (Ed.). Sydney: The Australian Computer Society, pp. 167-171.

McLachlan, G.J., Ambroise, C., Ben-Tovim Jones, L., and Zhu, J. (2004). Supervised learning methods for gene-expression data. In Computing Science and Statistics Vol. 36. Fairfax Station, Virginia: Interface Foundation of North America.

McLachlan, G.J., Ng, S.K., and Bean, R.W. (2004). Robust mixture modeling. Proceedings of the American Statistical Association, Physical and Engineering Sciences Section [CD-ROM], Toronto, August 2004. Alexandria, Virginia: American Statistical Association, pp. 2044-2055.

Ng, S.K., Krishnan, T., and McLachlan, G.J. (2004). The EM algorithm. In Handbook of Computational Statistics Vol. 1, J. Gentle, W. Hardle, and Y. Mori (Eds.). New York: Springer-Verlag, pp. 137-168.

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, 738-749.

Ng, S.K. and McLachlan, G.J. (2004). Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance images. Pattern Recognition 37, 1573-1589.

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, 2729-2744.

Kim, S.-G., Ng, S.K., McLachlan, G.J., and Wang, D. (2003). Segmentation of brain MR images with bias-field correction. In Proceedings of WDIC2003, APRS Workshop on Digital Image Computing, B.C. Lovell and A. Maeder (Eds.). Brisbane: Australian Pattern Recognition Society, pp. 3-8.

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, 579-592.

Mar, J.C. and McLachlan, G.J. (2003). Model-based clustering in gene expression microarrays: an application to breast cancer data. In Conferences in Research and Practice in Information Technology Vol. 19, Y.-P. Chen (Ed.). Sydney: The Australian Computer Society, pp. 139-144.

McLachlan, G.J., Ng, S.K., and Peel, D. (2003). On clustering by mixture models. In Studies in Classification, Data Analysis, and Knowledge Organization: Exploratory Data Analysis in Empirical Research, O. Opitz and M. Schwaiger (Eds.). Berlin: Springer-Verlag, pp. 141-148.

McLachlan, G.J., Peel, D., and Bean, R.W. (2003). Modelling high-dimensional data by mixtures of factor analyzers. Computational Statistics & Data Analysis 41, 379-388.

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, 45-55.

Ng, S.K.and McLachlan, G.J. (2003). An EM-based semiparametric mixture model approach to the regression analysis of competing-risks data. Statistics in Medicine 22, 1097-111.

Ng, 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, 143-161.

Ng, S.K. and McLachlan, G.J. (2003). Robust estimation in Gaussian mixtures using multiresolution kd-trees. In Proceedings of DICTA 2003, 7th Conference of Digital Image Computing: Techniques and Applications Vol. 1, C. Sun, H. Talbot, S. Ourselin, and T. Adriaansen (Eds.). Sydney: Australian Pattern Recognition Society, pp. 145-154.

Ambroise, C. and McLachlan, G.J. (2002). Selection bias in gene extraction on the basis of microarray gene expression data. Proceedings of the National Academy of Sciences USA 99, 6562-6566.

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, 7-34.

McLachlan, G.J., Bean, R.W., and Peel, D. (2002). A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18, 413-422.

McLachlan, G.J. and Hamaty, K.L. (2002). Contribution to the discussion of paper by N. Wang and A.E. Raftery. Journal of the American Statistical Association 97, 1009-1011.

Ng, 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. In Proceedings of DICTA 2002, 6th Conference of Digital Image Computing: Techniques and Applications, D. Suter and A. Bab-Hadiashar (Eds.). Melbourne: Australian Pattern Recognition Society, pp. 116-121.

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

McLachlan, G.J. (2001). Multivariate analysis: Classification and discriminant analysis. In International Encyclopedia of Social and Behavioral Sciences Vol. 15, N.J. Smelser and P.B. Baltes (Eds.). Oxford: Elsevier Science, pp. 10214-10218.

McLachlan, G.J. (2001). On the grouping of treatments following an ANOVA. (Letter to the Editor). Journal of Agricultural, Biological, and Environmental Statistics 6, 302-304.

Peel, D., Whiten, W.J., and McLachlan, G.J. (2001). Fitting mixtures of Kent distributions to aid in joint set identification. Journal of the American Statistical Association 96, 56-63.

Rose, S.E., Chalk, J.B., Griffin, M.P., Janke, A.L., Chen, F., McLachlan, G.J., Peel, D., Zelaya, F.O., Markus H.S., Jones, D.K., Simmons, A., O'Sullivan M., Jarosz, J.M., Strugnell, W., Doddrell, D.M., and Semple, J. (2001). MRI based diffusion and perfusion predictive model to estimate stroke evolution. Magnetic Resonance Imaging 19, 1043-1053.

McLachlan, G.J. and Peel, D. (2000). Finite Mixture Models. New York: Wiley. xxii + 419 pp.

McLachlan, G.J. and Peel, D. (2000). Mixtures of factor analyzers. In Proceedings of the Seventeenth International Conference on Machine Learning, P. Langley (Ed.). San Francisco: Morgan Kaufmann, pp. 599-606.

McLachlan, G.J. and Peel, D. (2000). On computational aspects of clustering via mixtures of normal and t-components. Proceedings of the American Statistical Association (Bayesian Statistical Science Section), Indianapolis, August 2000. Alexandria, Virginia: American Statistical Association.

McLaren, C.E., Cadez, I.V., Smyth, P., and McLachlan, G.J. (2000). Multivariate mixture models for classification of anemias. Proceedings of the American Statistical Association (Biometrics Section), Indianapolis, August 2000. Alexandria, Virginia: American Statistical Association, pp. 112-117.

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 hematological data by multiple linear regression and mixture distribution modeling. Statistics in Medicine 19, 83-98.

Peel, D. and McLachlan. G.J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing 10, 335-344.

Welham, J., McLachlan, G.J., Davies, G., and McGrath, J. (2000). Heterogeneity in schizophrenia; mixture modelling of age-at-first-admission, gender and diagnosis. Acta Psychiatrica Scandinavica 10, 312-317.

Cadez, I.V., McLaren, C.E., Smyth, P., and McLachlan, G.J. (1999). Hierarchical models for screening of iron deficiency anemia. In Proceedings of the Sixteenth International Conference on Machine Learning, I. Bratko and S. Dzeroski (Eds.). San Francisco: Morgan Kaufmann, pp. 77-86.

McLachlan, G.J. (1999). Mahalanobis Distance. Resonance 4, 20-26.

McLachlan, G.J. and Peel, D. (1999). Computing issues for the EM algorithm in mixture models. In Computing Science and Statistics Vol. 31, K. Berk and M. Pourahmadi (Eds.). Fairfax Station, Virginia: Interface Foundation of North America, pp. 421-430.

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, No. 2.

Ng, S.K., McLachlan, G.J., McGiffin, D.C., and O'Brien, M.F. (1999). Constrained mixture models in competing risks problems. Environmetrics 10, 753--767.

Feelders, A.J., Chang, S., and McLachlan, G.J. (1998). Mining in the presence of selectivity bias and its application to reject inference. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, R. Agrawal, P. Stolorz, and G. Piatetsky-Shapiro (Eds.). Menlo Park, California: AAAI Press, pp. 199-203.

McDonald, S. and McLachlan, G.J. (1998). Training feed-forward neural networks through hidden representations using the EM algorithm. Proceedings of the Second ASOR Queensland Conference, Brisbane, December 1998. Brisbane: Australian Society for Operations Research, pp. 133--149.

McLachlan, G.J. and Peel, D. (1998). Robust cluster analysis via mixtures of multivariate t-distributions. In Lecture Notes in Computer Science Vol. 1451, A. Amin, D. Dori, P. Pudil, and H. Freeman (Eds.). Berlin: Springer-Verlag, pp. 658-666.

McLachlan, G.J. and Peel, D. (1998). MIXFIT: an algorithm for the automatic fitting and testing of normal mixture models. In Proceedings of the 14th International Conference on Pattern Recognition Vol. I, A.K. Jain, S. Venkatesh, and B. Lovell (Eds.). Los Alamitos, California: IEEE Computer Society, pp. 553-557.

McLachlan, G.J. and Peel, D. (1998). Mixture models and neural networks for clustering. In Proceedings of the Ninth Australian Conference on Neural Networks, T. Downs, M. Frean, and M. Gallagher (Eds.). Brisbane: Department of Computer Science and Electrical Engineering, University of Queensland, pp. 109-113.

McLaren, C.E., McLachlan, G.J., Halliday, J.W., Webb, S.I., Leggett, B.A., Jazwinska, E.C., Crawford, D.H.G., Gordeuk, V.R., McLaren, G.D., and Powell, L.W. (1998). The distribution of transferrin saturation and hereditary haemochromatosis in Australians. Gastroenterology 114, 543-549.

Ng, S.K. and McLachlan, G.J. (1998). On modifications to the long-term survival mixture model in the presence of competing risks. Journal of Statistical Computation and Simulation 61, 77-96.

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

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, 169-179.

Hawkins, D.M. and McLachlan, G.J. (1997). High-breakdown linear discriminant analysis. Journal of the American Statistical Association 92, 136-143.

McGiffin, D.C., Galbraith, A.J., O'Brien, M.F., McLachlan, G.J., Naftel, D.C., Adams, P., Reddy, S., and Early, L. (1997). An analysis of valve re-replacement following aortic valve replacement with biological devices. Journal of Thoracic and Cardiovascular Surgery 113, 311-318.

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

McLachlan, G.J. and Krishnan, T. (1997). The EM Algorithm and Extensions. New York: Wiley. xvii + 274 pp.

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, No. 7.

McLachlan, G.J. and Peel, D. (1997). Contribution to the discussion of paper by S. Richardson and P.J. Green. Journal of the Royal Statistical Society Series B 59, 779-780.

McLachlan, G.J. and Peel, D. (1997). On a resampling approach to choosing the number of components in normal mixture models. In Computing Science and Statistics Vol. 28, L. Billard and N.I. Fisher (Eds.). Fairfax Station, Virginia: Interface Foundation of North America, pp. 260-266.

McLachlan, G.J., Peel, D., and Prado, P. (1997). Clustering via normal mixture models. Proceedings of the American Statistical Association (Bayesian Statistical Science Section), Anaheim, August 1997. Alexandria, Virginia: American Statistical Association, pp. 98-103.

McLachlan, G.J. (1996). On Aitken's method and other approaches for accelerating convergence of the EM algorithm. Proceedings of the A.C. Aitken Centenary Conference, University of Otago, August 1995. Dunedin: University of Otago Press, pp. 201-209.

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

McLachlan, G.J., Ng, S.K., Galloway, G., and Wang, D. (1996). Clustering of magnetic resonance images. Proceedings of the American Statistical Association (Statistical Computing Section), Chicago, August 1996. Alexandria, Virginia: American Statistical Association, pp. 12-17.

McLachlan, G.J., Ng, S.K., Galloway, G., and Wang, D. (1996). A mixture model-based approach to the segmentation of MR images of the human brain. Proceedings of Image Segmentation Workshop, University of Technology Sydney, December 1996. Sydney: Australian Pattern Recognition Society, pp. 33-40.

McLachlan, G.J. and Peel, D. (1996). An algorithm for unsupervised learning via normal mixture models. In ISIS: Information, Statistics and Induction in Science, D.L. Dowe, K.B. Korb, and J.J. Oliver (Eds.). Singapore: World Scientific Publishing, pp. 354-363.

McLachlan G.J., Peel, D., and Whiten, W.J. (1996). Maximum likelihood clustering via finite mixture models. Signal Processing: Image Communication 8, 105-111.

McLachlans, G.J. (1995). Mixtures -- models and applications. In The Exponential Distribution: Theory, Methods, and Applications, N. Balakrishnan and A.P. Basu (Eds.). Basel: Gordon & Breach, pp. 307-315.

McLachlan, G.J., McLaren, C.E., and Matthews, D. (1995). An algorithm for the likelihood ratio test of one versus two components in a mixture model fitted to grouped and truncated data. Communications in Statistics - Simulation and Computation 24, 965-985.

McLachlan, G.J. and Scot, D. (1995). On the asymptotic relative efficiency of the linear discriminant function under partial nonrandom classification of the training data. Journal of Statistical Computation and Simulation 52, 415-426.

Ng. S.K., McLachlan, G.J., Galloway, G., and Rose. S.E. (1995). A mixture model approach to segmentation of magnetic resonance images. In Proceedings of DICTA 95, 3rd Conference of Digital Image Computing: Techniques and Applications, A. Maeder and B. Lovell (Eds.). Brisbane: Australian Pattern Recognition Society, pp. 588-593.

Lawoko, C.R.O. and McLachlan (1994). Estimation of mixing proportions in the presence of autoregressively correlated training data. Communications in Statistics - Simulation and Computation 23, 591-613.

McLachlan, G.J. (1994). One hundred years of mixtures. Stats No. 12, 6-12.

McLachlan, G.J. (1994). Contribution to the discussion of paper by B.D. Ripley. Journal of the Royal Statistical Society Series B 56, 447-448.

McLachlan, G.J. and McGiffin, D.C. (1994). On the role of finite mixture models in survival analysis. Statistical Methods in Medical Research 3, 211-226.

Ray, M.J., Hawson, G.A.T., Just, S.J.E., McLachlan, G.J., and O'Brien, M.F. (1994). The relationship of platelet aggregation to bleeding after cardiopulmonary bypass surgery. Annals of Thoracic Surgery 57, 981-986.

Shoukri, M.M. and McLachlan, G.J. (1994). Parametric estimation in a genetic mixture model with application to nuclear family data. Biometrics 50, 128-139.

McLachlan, G.J. (1993). On a connection between the logit model, normal discriminant analysis, and multivariate normal mixtures (Letter to the Editor). American Statistician 47, 18.

McGiffin, D.C., O'Brien, M.F., Galbraith, A.J., McLachlan, G.J., Stafford, E.G., Gardiner, M.A.H., Pohlner, P.G., Early, L., and Kear, L. (1993). An analysis of risk factors for death and mode-specific death following aortic valve replacement using allograft, xenograft and mechanical valves. Journal of Thoracic and Cardiovascular Surgery 106, 895-911.

Jones, P.N. and McLachlan, G.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, 31-44.

Jones, P.N. and McLachlan, G.J. (1992). Fitting finite mixture models in a regression context. Australian Journal of Statistics 34, 233-240.

McGiffin, D.C., Galbraith, A.J., McLachlan, G.J., Stower, R.E., Wong, M.C., Stafford, E.G., Gardner, M.A.H., Pohlner, P.G., and O'Brien, M.F. (1992). Aortic valve infection--risk factors for death and recurrent endocarditis following aortic valve replacement. Journal of Thoracic and Cardiovascular Surgery 104, 511-520.

McLachlan, G.J. (1992). Discriminant Analysis and Statistical Pattern Recognition. New York: Wiley. xv + 526 pp.

McLachlan, G.J. (1992). The use of cluster analysis and related techniques in medicine. Statistical Methods in Medical Research 1, 27-48.

Jones, P.N. and McLachlan, G.J. (1991). Fitting mixture distributions to Phenylthiocarbamide (PTC) sensitivity. American Journal of Human Genetics 48, 117-120.

McGiffin, D.C. and McLachlan, G.J. (1991). The analysis of time-related events after cardiac surgery. Australian Journal of Cardiac and Thoracic Surgery 1, 11-13.

O'Brien, M.F., McGiffin, D.C., Stafford, E.G., Gardner, M.A.H., Pohlner, P.G., 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^o C stored valves. Journal of Cardiac Surgery 6, 534-543.

Basford, K.E. and McLachlan, G.J. (1990). Cluster analysis of three-way data for the interpretation of agricultural adaptation experiments. In Analysis of Data from Agricultural Adaptation Experiments, I.H. DeLacy (Ed.). Bangkok: Australian Cooperation with the Thai World Bank National Agricultural Research Project, pp. 186-184.

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

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

Jones, P.N. and McLachlan, G.J. (1989). Modelling mass-size particle data by finite mixtures. Communications in Statistics - Theory and Methods 18, 2629-2646.

Lawoko, C.R.O. and McLachlan, G.J. (1989). Bias associated with the discriminant analysis approach to the estimation of mixing proportions. Pattern Recognition 22, 763-766.

McLachlan, G.J. and Gordon, R.D. (1989). Mixture models for partially unclassified data: a case study of renal venous renin levels in essential hypertension. Statistics in Medicine 8, 1291-1300.

Lawoko, C.R.O. and McLachlan, G.J. (1988). Further results on discrimination with autocorrelated observations. Pattern Recognition 21, 69-72.

McLachlan, G.J. (1988). On the choice of starting values for the EM algorithm in fitting mixture models. Statistician 37, 417-425.

McLachlan, G.J. and Basford, K.E. (1988). Mixture Models: Inference and Applications to Clustering. New York: Marcel Dekker. xi + 259 pp.

McLachlan, G.J. and Jones, P.N. (1988). Fitting mixture models to grouped and truncated data via the EM algorithm. Biometrics 44, 571-578.

McLachlan, G.J. (1987). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. Journal of the Royal Statistical Society Series C (Applied Statistics) 36, 318-324.

McLachlan, G.J. (1987). Error rate estimation in discriminant analysis: recent advances. In Advances in Multivariate Statistical Analysis, A.K. Gupta (Ed.). Dordrecht: Reidel, pp. 233-252.

Quinn, B.G., McLachlan, G.J., and Hjort, N.L. (1987). A note on the Aitkin-Rubin approach to hypothesis testing in mixture models. Journal of the Royal Statistical Society Series B 49, 311-314.

Lawoko, C.R.O. and McLachlan, G.J. (1986). Asymptotic error rates of the W and Z statistics when the training observations are dependent. Pattern Recognition 19, 467-471.

McLachlan, G.J. (1986). Assessing the performance of an allocation rule. Computers & Mathematics with Applications} 12A, 261-272. Reprinted in Statistical Methods of Discrimination and Classification: Advances in Theory and Applications, S.C. Choi (Ed.). New York: Pergamon Press, pp. 261-272.

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

Basford, K.E. and McLachlan, G.J. (1985). Likelihood estimation for normal mixture models. Journal of the Royal Statistical Society Series C (Applied Statistics) 34, 282-289.

Basford, K.E. and McLachlan, G.J. (1985). The mixture method of clustering applied to three-way data. Journal of Classification 2, 109-125.

Basford, K.E. and McLachlan, G.J. (1985). Cluster analysis in a randomized complete block design. Communications in Statistics - Theory and Methods 14, 451-463.

Lawoko, C.R.O. and McLachlan, G.J. (1985). Discrimination with autocorrelated observations. Pattern Recognition 8, 145-149.

McLachlan, G.J. (1985). Asymptotic conditional biases of logistic regression parameters (Letter to the Editor). Statistics in Medicine 4, 244-245.

Do, K. and McLachlan, G.J. (1984). Estimation of mixing proportions: a case study. Journal of the Royal Statistical Society Series C (Applied Statistics) 33, 134-140.

Basford, K.E. and McLachlan, G.J. (1983). On computational aspects associated with bias correction techniques in a cluster analysis context. Proceedings of STATCOMP 83, Sydney, July 1983. Sydney: Statistical Society of Australia (Statistical Computing Section), pp. 31-39.

Lawoko, C.R.O. and McLachlan, G.J. (1983). Some asymptotic results on the effect of autocorrelation on the error rates of the sample linear discriminant function. Pattern Recognition 16, 119-121.

McLachlan, G.J. (1982). The classification and mixture maximum likelihood approaches to cluster analysis. In Handbook of Statistics Vol. 2, P.R. Krishnaiah and L. Kanal (Eds.). Amsterdam: North-Holland, pp. 199-208.

McLachlan, G.J. (1982). On the bias and variance of some proportion estimators. Communications in Statistics - Simulation and Computation 11, 715-726.

McLachlan, G.J. and Ganesalingam, S. (1982). Updating a discriminant function on the basis of unclassified data. Communications in Statistics - Simulation and Computation 11, 753-767.

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, 331-334.

Ganesalingam, S. and McLachlan, G.J. (1981). Some efficiency results on the estimation of the mixing proportion in a mixture of two normal distributions. Biometrics 37, 23-33.

Byth, K. and McLachlan, G.J. (1980). Logistic regression compared to normal discrimination for non-normal populations. Australian Journal of Statistics 22, 188-196.

Ganesalingam, S. and McLachlan, G.J.(1980). Error rate estimation on the basis of posterior probabilities. Pattern Recognition 11, 405-413.

Ganesalingam, S. and McLachlan, G.J. (1980). A comparison of the mixture and classification approaches to cluster analysis. Communications in Statistics - Theory and Methods A9, 923-933.

McLachlan, G.J. (1980). A note on bias correction in maximum likelihood estimation with logistic discrimination. Technometrics 22, 621-627.

McLachlan, G.J. (1980). On the relationship between the F-test and the overall error rate for variable selection in two-group discriminant analysis. Biometrics 36, 501-510.

McLachlan, G.J. (1980). The efficiency of Efron's bootstrap approach applied to error rate estimation in discriminant analysis. Journal of Statistical Computation and Simulation 10, 273-279.

McLachlan, G.J. (1980). Selection of variables in discriminant analysis (Letter to the Editor). Biometrics 36, 554.

McLachlan, G.J. (1980). On the mean square error associated with adaptive generalized ridge regression. Biometrical Journal 22, 125-129.

McLachlan, G.J. (1980). Estimation of mixing proportions by the EM algorithm. Proceedings of the American Statistical Association (Statistical Computing Section), Houston, August 1980. Washington, D.C.: American Statistical Association, pp. 140-143.

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, 237-249.

Ganesalingam, S. and McLachlan, G.J. (1979). A case study of two clustering methods based on maximum likelihood. Statistica Neerlandica 33, 81-90.

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, 151-158.

McLachlan, G.J. (1979). A comparison of the estimative and predictive methods of estimating posterior probabilities. Communications in Statistics - Theory and Methods A8, 919-929.

McLachlan, G.J. and Byth, K. (1979). Expected error rates for logistic regression versus normal discriminant analysis. Biometrical Journal 21, 47-56.

Byth, K. and McLachlan, G.J. (1978). The biases associated with maximum likelihood methods of estimation of the multivariate logistic risk function. Communications in Statistics - Theory and Methods A7, 877-890.

Ganesalingam, S. and McLachlan, G.J. (1978). The efficiency of a linear discriminant function based on unclassified initial samples. Biometrika 65, 658-662.

McLachlan, G.J. (1978). Small sample results for partial classification with the Studentized statistic W. Biometrical Journal 20, 639-644.

McLachlan, G.J. (1977). Estimating the linear discriminant function from initial samples containing a small number of unclassified observations. Journal of the American Statistical Association 72, 403-406.

McLachlan, G.J. (1977). A note on the choice of a weighting function to give an efficient method for estimating the probability of misclassification. Pattern Recognition 8, 147-149.

McLachlan, G.J. (1977). Constrained sample discrimination with the Studentized classification statistic W. Communications in Statistics - Theory and Methods A6, 575-583.

McLachlan, G.J. (1977). The bias of sample based posterior probabilities. Biometrical Journal 19, 421-426.

McLachlan, G.J. (1976). The bias of the apparent error rate in discriminant analysis. Biometrika 63, 239-244.

McLachlan, G.J. (1976). A criterion for selecting variables for the linear discriminant function. Biometrics 32, 529-535.

McLachlan, G.J. (1976). Further results on the effect of intraclass correlation among training samples in discriminant analysis. Pattern Recognition 7, 273-275.

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, 365-369.

McLachlan, G.J. (1975). Confidence intervals for the conditional probability of misallocation in discriminant analysis. Biometrics 31, 161-1267.

McLachlan, G.J. (1975). Some expected values for the error rates of the sample quadratic discriminant function. Australian Journal of Statistics 17, 161-165.

McLachlan, G.J. (1974). The asymptotic distributions of the conditional error rate and risk in discriminant analysis. Biometrika 61, 131-135.

McLachlan, G.J. (1974). Estimation of the errors of misclassification on the criterion of asymptotic mean square error. Technometrics 16, 255-260.

McLachlan, G.J. (1974). An asymptotic unbiased technique for estimating the error rates in discriminant analysis. Biometrics 30, 239-249.

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, 569-575.

McLachlan, G.J. (1973). An asymptotic expansion of the expectation of the estimated error rate in discriminant analysis. Australian Journal of Statistics 15, 210-214.

McLachlan, G.J. (1972). Asymptotic results for discriminant analysis when the initial samples are misclassified. Technometrics 14, 415-422.

McLachlan, G.J. (1972). An asymptotic expansion for the variance of the errors of misclassification of the linear discriminant function. Australian Journal of Statistics 14, 68-72.