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2020 Journal Article Approximation by finite mixtures of continuous density functions that vanish at infinityNguyen, T. Tin, Nguyen, Hien D., Chamroukhi, Faicel and McLachlan, Geoffrey J. (2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics and Statistics, 7 (1) 1750861. doi: 10.1080/25742558.2020.1750861 |
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2020 Journal Article Mini-batch learning of exponential family finite mixture modelsNguyen, Hien D., Forbes, Florence and McLachlan, Geoffrey J. (2020). Mini-batch learning of exponential family finite mixture models. Statistics and Computing, 30 (4), 731-748. doi: 10.1007/s11222-019-09919-4 |
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2020 Journal Article A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fractionTawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2020). A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction. Biometrics, 76 (3) biom.13202, 753-766. doi: 10.1111/biom.13202 |
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2020 Book Chapter Comprehensive chemometrics: chemical and biochemical data analysisMcLachlan, G. J., Rathnayake, S. and Lee, S. X. (2020). Comprehensive chemometrics: chemical and biochemical data analysis. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 267-304) edited by Steven Brown, Roma Tauler and Beata Walczak. Oxford, United Kingdom: Elsevier. |
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2020 Book Chapter Model-based clusteringMcLachlan, G. J., Rathnayake, S. I. and Lee, S. X. (2020). Model-based clustering. Comprehensive chemometrics: chemical and biochemical data analysis. (pp. 509-529) edited by Steven Brown, Romà Tauler and Beata Walczak. Amsterdam, Netherlands: Elsevier. doi: 10.1016/B978-0-12-409547-2.14649-9 |
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2020 Conference Publication Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-RiskLee, Sharon X. and McLachlan, Geoffrey J. (2020). Modelling asset return using multivariate asymmetric mixture models with applications to estimation of Value-at-Risk. 20th International Congress on Modelling and Simulation - Adapting to Change: The Multiple Roles of Modelling, MODSIM 2013 , Adelaide, SA, Australia, 1 - 6 December 2013. Modelling and Simulation Society of Australia and New Zealand Inc. (MSSANZ). |
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2019 Journal Article On approximations via convolution-defined mixture modelsNguyen, Hien D. and McLachlan, Geoffrey (2019). On approximations via convolution-defined mixture models. Communications in Statistics - Theory and Methods, 48 (16), 3945-3955. doi: 10.1080/03610926.2018.1487069 |
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2019 Journal Article False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging studyNguyen, Hien D., Yee, Yohan, McLachlan, Geoffrey J. and Lerch, Jason P. (2019). False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study. SORT, 43 (2), 1-22. doi: 10.2436/20.8080.02.87 |
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2019 Journal Article A multilevel survival model with random covariates and unobservable random effectsTawiah, Rchard, Yau, Kelvin K. W., McLachlan, Geoffrey J., Chambers, Suzanne and Ng, Shu-Kay (2019). A multilevel survival model with random covariates and unobservable random effects. Statistics in Medicine, 38 (6), 1036-1055. doi: 10.1002/sim.8041 |
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2019 Conference Publication PPEM: privacy-preserving EM learning for mixture modelsLee, Sharon X., Leemaqz, Kaleb L. and McLachlan, Geoffrey J. (2019). PPEM: privacy-preserving EM learning for mixture models. 8th International Conference on Applications and Techniques in Information Security, ATIS 2017, Auckland, New Zealand, 6-7 July 2017. Oxford, United Kingdom: John Wiley & Sons. doi: 10.1002/cpe.5208 |
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2019 Journal Article Finite mixture modelsMcLachlan, Geoffrey J., Lee, Sharon X. and Rathnayake, Suren I. (2019). Finite mixture models. Annual Review of Statistics and Its Application, 6 (1), 355-378. doi: 10.1146/annurev-statistics-031017-100325 |
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2019 Journal Article Mixture cure models with time-varying and multilevel frailties for recurrent event dataTawiah, Richard, McLachlan, Geoffrey J. and Ng, Shu Kay (2019). Mixture cure models with time-varying and multilevel frailties for recurrent event data. Statistical Methods in Medical Research, 29 (5) 0962280219859377, 096228021985937-1385. doi: 10.1177/0962280219859377 |
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2019 Conference Publication Positive data kernel density estimation via the LogKDE package for RJones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2019). Positive data kernel density estimation via the LogKDE package for R. AusDM 2018: 16th Australasian Conference on Data Mining, Bahrurst, NSW, Australia, 28 - 30 November 2018. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-13-6661-1_21 |
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2019 Conference Publication Flexible modelling via multivariate skew distributionsMcLachlan, Geoffrey J. and Lee, Sharon X. (2019). Flexible modelling via multivariate skew distributions. Research School on Statistics and Data Science (RSSDS 2019), Melbourne, VIC, Australia, 24–26 July 2019. Singapore, Singapore: Springer Singapore. doi: 10.1007/978-981-15-1960-4_4 |
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2019 Book Chapter Mixture of factor analyzers for the clustering and visualization of high-dimensional dataMcLachlan, Geoffrey J., Baek, Jangsun and Rathnayake, Suren I. (2019). Mixture of factor analyzers for the clustering and visualization of high-dimensional data. Advances in latent class analysis: a festschrift in honor of C. Mitchell Dayton. (pp. 79-98) edited by Gregory R. Hancock, Jeffrey R. Harring and George B. Macready. Charlotte, NC, United States: Information Age Publishing. doi: 10.1108/978-1-64113-563-420251006 |
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2019 Journal Article Skew-normal generalized spatial panel data modelFarzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza and McLachlan, Geoffrey J. (2019). Skew-normal generalized spatial panel data model. Communications in Statistics: Simulation and Computation, 50 (11), 1-29. doi: 10.1080/03610918.2019.1622718 |
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2019 Journal Article Skew-normal Bayesian spatial heterogeneity panel data modelsFarzammehr, Mohadeseh Alsadat, Zadkarami, Mohammad Reza, McLachlan, Geoffrey J. and Lee, Sharon X. (2019). Skew-normal Bayesian spatial heterogeneity panel data models. Journal of Applied Statistics, 47 (5), 1-23. doi: 10.1080/02664763.2019.1657812 |
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2018 Journal Article Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling frameworkNg, Shu-Kay, Tawiah, Richard and McLachlan, Geoffrey J. (2018). Unsupervised pattern recognition of mixed data structures with numerical and categorical features using a mixture regression modelling framework. Pattern Recognition, 88, 261-271. doi: 10.1016/j.patcog.2018.11.022 |
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2018 Journal Article Randomized mixture models for probability density approximation and estimationNguyen, Hien D., Wang, Dianhui and McLachlan, Geoffrey J. (2018). Randomized mixture models for probability density approximation and estimation. Information Sciences, 467, 135-148. doi: 10.1016/j.ins.2018.07.056 |
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2018 Journal Article logKDE: log-transformed kernel density estimationJones, Andrew T., Nguyen, Hien D. and McLachlan, Geoffrey J. (2018). logKDE: log-transformed kernel density estimation. Journal of Open Source Software, 3 (28) 870, 870. doi: 10.21105/joss.00870 |
