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2005 Conference Publication Application of mixture models to detect differentially expressed genesJones, 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 |
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2005 Journal Article Cluster analysis of high-dimensional data: A case studyBean, 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. |
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2005 Book Chapter Use of microarray data via model-based classification in the study and prediction of survival from lung cancerJones, 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 |
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2005 Conference Publication Mixture Model-based Statistical Pattern Recognition of Clustered or Longitudinal DataNg, 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. |
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2004 Journal Article Modelling the Distribution of Ischaemic Stroke-Specific Survival Time Using an EM-based Mixture Approach with Random Effects AdjustmentNg, 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 |
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2004 Journal Article Speeding up the EM algorithm for mixture model-based segmentation of magnetic resonance imagesNg, 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 |
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2004 Journal Article Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass ClassificationNg, 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 |
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2004 Book Chapter The EM algorithmNg, 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. |
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2004 Book Analyzing microarray gene expression dataMcLachlan, Geoffrey J., Do, Kim-Anh and Ambroise, Christophe (2004). Analyzing microarray gene expression data. Hoboken, NJ, USA: John Wiley & Sons. doi: 10.1002/047172842x |
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2004 Journal Article On a resampling approach for tests on the number of clusters with mixture model-based clustering of tissue samplesMcLachlan, 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 |
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2004 Conference Publication Linking gene-expression experiments with survival-time dataJones, 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. |
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2004 Book Analyzing Microarray Gene Expression DataMcLachlan, G. J., Do, K. and Ambroise, C (2004). Analyzing Microarray Gene Expression Data. New York: Wiley-Interscience. |
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2004 Journal Article Clustering objects on subsets of attributes - DiscussionHand, 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. |
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2004 Conference Publication On the simultaneous use of clinical and microarray expression data in the cluster analysis of tissue samplesMcLachlan, 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. |
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2004 Journal Article Mixture modelling for cluster analysisMcLachlan, 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 |
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2003 Journal Article Model-based clustering in gene expression microarrays: an application to breast cancer dataMar, 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 |
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2003 Journal Article Model-based clustering in gene expression microarrays: an application to breast cancer dataMar, 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 |
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2003 Journal Article An EM-based Semi-Parametric Mixture Model Approach to the Regression Analysis of Competing-Risks DataNg, 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 |
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2003 Journal Article On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal MixturesNg, 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 |
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2003 Journal Article Modelling High-Dimensional Data by Mixtures of Factor AnalyzersMcLachlan, 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 |
