|
2018 Journal Article Chunked-and-averaged estimators for vector parametersNguyen, 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 |
|
2018 Journal Article EMMIXcskew: an R package for the fitting of a mixture of canonical fundamental skew t-distributionsLee, 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 |
|
2018 Book Chapter Risk measures based on multivariate skew normal and skew t-mixture modelsLee, 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 |
|
2017 Journal Article Whole-volume clustering of time series data from zebrafish brain calcium images via mixture modelingNguyen, 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 |
|
2017 Journal Article Deep Gaussian mixture modelsViroli, Cinzia and McLachlan, Geoffrey J. (2017). Deep Gaussian mixture models. Statistics and Computing, 29 (1), 1-9. doi: 10.1007/s11222-017-9793-z |
|
2017 Journal Article Some theoretical results regarding the polygonal distributionNguyen, 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 |
|
2017 Journal Article Finite mixture models in biostatisticsLee, Sharon X., Ng, Shu-Kay and McLachlan, Geoffrey J. (2017). Finite mixture models in biostatistics. Handbook of Statistics, 36, 75-102. |
|
2017 Journal Article Robust mixtures of factor analysis models using the restricted multivariate skew-t distributionLin, 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 |
|
2017 Journal Article Maximum pseudolikelihood estimation for model-based clustering of time series dataNguyen, 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 |
|
2017 Conference Publication Corruption-resistant privacy preserving distributed EM algorithm for model-based clusteringLeemaqz, 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 |
|
2017 Book Chapter Statistical evaluation of labeled comparative profiling proteomics experiments using permutation testNguyen, 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 |
|
2017 Book Chapter Finite mixture models in biostatisticsLee, 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 |
|
2017 Conference Publication Privacy distributed three-party learning of Gaussian mixture modelsLeemaqz, 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 |
|
2017 Conference Publication On the identification of correlated differential features for supervised classification of high-dimensional dataNg, 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 |
|
2017 Book Chapter ClusteringMcLachlan, 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 |
|
2017 Conference Publication Iteratively-reweighted least-squares fitting of support vector machines: a majorization–minimization algorithm approachNguyen, 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. |
|
2017 Book Chapter On the identification of correlated differential features for supervised classification of high-dimensional dataNg, 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 |
|
2016 Journal Article Partial identification in the statistical matching problemAhfock, 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 |
|
2016 Journal Article Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency dataLloyd-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 |
|
2016 Journal Article A universal approximation theorem for mixture-of-experts modelsNguyen, 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 |
