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2006 Journal Article A Score Test for Zero-Inflation in Correlated Count DataXiang, 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 |
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2006 Journal Article A Mixture model with random-effects components for clustering correlated gene-expression profilesNg, 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 |
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2006 Journal Article Selection bias in working wit the top genes in supervised classification of tissue samplesZhu, 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 |
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2006 Journal Article Multi-level zero-inflated Poisson regression modelling of correlated count data with excess zerosLee, 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 |
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2006 Journal Article An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource UtilizationNg, 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 |
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2006 Journal Article Robust cluster analysis via mixture modelsMcLachlan, 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. |
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2006 Journal Article A simple implementation of a normal mixture approach to differential gene expression in multiclass microarraysMcLachlan, 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 |
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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 Journal Article Using mixture models to detect differentially expressed genesMcLachlan, 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 |
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2005 Journal Article Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysisKerr, 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 |
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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 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 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 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 |
