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2010 Book Chapter Use of mixture models in multiple hypothesis testing with applications in bioinformaticsMcLachlan, 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 |
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2009 Journal Article A score test for assessing the cured proportion in the long-term survivor mixture modelZhao, 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 |
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2009 Conference Publication On a general method for matrix factorisation applied to supervised classificationNikulin, 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 |
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2009 Journal Article Automated high-dimensional flow cytometric data analysisPyne, 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 |
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2009 Conference Publication Regularised k-means clustering for dimension reduction applied to supervised classificationNikulin, 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. |
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2009 Journal Article Microarray data analysis for differential expression: a tutorialSuarez, 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. |
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2009 Book Chapter Statistical analysis on microarray data: selection of gene prognosis signaturesLe 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 |
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2009 Conference Publication Classification of imbalanced marketing data with balanced random setsNikulin, 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. |
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2009 Book Chapter Clustering methods for gene-expression dataFlack, 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 |
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2009 Conference Publication Ensemble approach for the classification of imbalanced dataNikulin, 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 |
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2009 Book Chapter Model-based clusteringMcLachlan, 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 |
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2009 Book Chapter EMMcLachlan, 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 |
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2009 Journal Article Classification of imbalanced marketing data with balanced random setsNikulin, Vladimir and McLachlan, Geoffrey J. (2009). Classification of imbalanced marketing data with balanced random sets. Journal of Machine Learning Research, 7, 89-100. |
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2009 Conference Publication Multivariate skew t mixture models: applications to fluorescence-activated cell sorting dataWang, 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 |
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2008 Journal Article Wallace's approach to unsupervised learning: The Snob programJorgensen, 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 |
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2008 Journal Article Professor Gopal Kanji's retirement as editor of Journal of Applied StatisticsAgrawal, 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 |
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2008 Book The EM algorithm and extensionsMcLachlan, Geoffrey J. and Krishnan, Thriyambakam (2008). The EM algorithm and extensions. 2nd ed. Hoboken, NJ, United States: John Wiley & Sons. doi: 10.1002/9780470191613 |
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2008 Book Chapter Correcting for Selection Bias via Cross-Validation in the Classification of Microarray DataMcLachlan, 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 |
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2008 Conference Publication Clustering via mixture regression models with random effectsMcLachlan, 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 |
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2008 Book Chapter ClusteringMcLachlan, 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 |
