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2024 Journal Article Functional mixtures-of-expertsChamroukhi, Faïcel, Pham, Nhat Thien, Hoang, Van Hà and McLachlan, Geoffrey J. (2024). Functional mixtures-of-experts. Statistics and Computing, 34 (3) 98. doi: 10.1007/s11222-023-10379-0 |
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2024 Journal Article Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in RLyu, Ziyang, Ahfock, Daniel, Thompson, Ryan and McLachlan, Geoffrey J. (2024). Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R. Australian and New Zealand Journal of Statistics, 66 (2), 146-162. doi: 10.1111/anzs.12413 |
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2024 Journal Article An overview of skew distributions in model-based clusteringLee, Sharon X. and McLachlan, Geoffrey J. (2024). An overview of skew distributions in model-based clustering. Science Talks, 9 100298, 100298. doi: 10.1016/j.sctalk.2024.100298 |
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2024 Journal Article A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopyKoh, Edwin J.Y., Amini, Eiman, Spier, Carlos A., McLachlan, Geoffrey J., Xie, Weiguo and Beaton, Nick (2024). A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy. Minerals Engineering, 205 108481, 1-16. doi: 10.1016/j.mineng.2023.108481 |
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2023 Journal Article Robust clustering based on finite mixture of multivariate fragmental distributionsMaleki, Mohsen, McLachlan, Geoffrey J. and Lee, Sharon X. (2023). Robust clustering based on finite mixture of multivariate fragmental distributions. Statistical Modelling, 23 (3), 247-272. doi: 10.1177/1471082X211048660 |
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2023 Journal Article Semi-supervised learning of classifiers from a statistical perspective: a brief reviewAhfock, Daniel and McLachlan, Geoffrey J. (2023). Semi-supervised learning of classifiers from a statistical perspective: a brief review. Econometrics and Statistics, 26, 124-138. doi: 10.1016/j.ecosta.2022.03.007 |
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2023 Journal Article Order selection with confidence for finite mixture modelsNguyen, Hien D., Fryer, Daniel and McLachlan, Geoffrey J. (2023). Order selection with confidence for finite mixture models. Journal of the Korean Statistical Society, 52 (1), 154-184. doi: 10.1007/s42952-022-00195-z |
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2023 Journal Article A new algorithm for support vector regression with automatic selection of hyperparametersWang, You-Gan, Wu, Jinran, Hu, Zhi-Hua and McLachlan, Geoffrey J. (2023). A new algorithm for support vector regression with automatic selection of hyperparameters. Pattern Recognition, 133 108989, 1-9. doi: 10.1016/j.patcog.2022.108989 |
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2023 Journal Article Approximation of probability density functions via location-scale finite mixtures in Lebesgue spacesNguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D. and McLachlan, Geoffrey J. (2023). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods, 52 (14), 1-12. doi: 10.1080/03610926.2021.2002360 |
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2023 Journal Article Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approachNg, Shu Kay, Tawiah, Richard, McLachlan, Geoffrey J. and Gopalan, Vinod (2023). Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach. Biostatistics, 24 (1), 108-123. doi: 10.1093/biostatistics/kxab037 |
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2022 Journal Article An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation modelsKoh, Edwin J. Y., Amini, Eiman, Gaur, Shruti, Becerra Maquieira, Miguel, Jara Heck, Christian, McLachlan, Geoffrey J. and Beaton, Nick (2022). An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models. Minerals Engineering, 189 107886, 107886. doi: 10.1016/j.mineng.2022.107886 |
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2022 Journal Article A spatial heterogeneity mixed model with skew-elliptical distributionsFarzammehr, Mohadeseh Alsadat and McLachlan, Geoffrey J. (2022). A spatial heterogeneity mixed model with skew-elliptical distributions. Communications for Statistical Applications and Methods, 29 (3), 373-391. doi: 10.29220/csam.2022.29.3.373 |
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2022 Journal Article Statistical file-matching of non-Gaussian data: a game theoretic approachAhfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2022). Statistical file-matching of non-Gaussian data: a game theoretic approach. Computational Statistics and Data Analysis, 168 107387, 1-16. doi: 10.1016/j.csda.2021.107387 |
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2022 Journal Article An overview of skew distributions in model-based clusteringLee, Sharon X. and McLachlan, Geoffrey J. (2022). An overview of skew distributions in model-based clustering. Journal of Multivariate Analysis, 188 104853, 1-14. doi: 10.1016/j.jmva.2021.104853 |
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2021 Journal Article Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts modelsNguyen, Hien Duy, Nguyen, TrungTin, Chamroukhi, Faicel and McLachlan, Geoffrey John (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications, 8 (1) 13. doi: 10.1186/s40488-021-00125-0 |
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2021 Journal Article Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopyKoh, Edwin J. Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising convolutional neural networks to perform fast automated modal mineralogy analysis for thin-section optical microscopy. Minerals Engineering, 173 107230, 107230. doi: 10.1016/j.mineng.2021.107230 |
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2021 Journal Article Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulationsKoh, Edwin J.Y., Amini, Eiman, McLachlan, Geoffrey J. and Beaton, Nick (2021). Utilising a deep neural network as a surrogate model to approximate phenomenological models of a comminution circuit for faster simulations. Minerals Engineering, 170 107026, 1-11. doi: 10.1016/j.mineng.2021.107026 |
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2021 Journal Article Data fusion using factor analysis and low-rank matrix completionAhfock, Daniel, Pyne, Saumyadipta and McLachlan, Geoffrey J. (2021). Data fusion using factor analysis and low-rank matrix completion. Statistics and Computing, 31 (5) 58. doi: 10.1007/s11222-021-10033-7 |
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2021 Journal Article Multi‐node expectation–maximization algorithm for finite mixture modelsLee, Sharon X., McLachlan, Geoffrey J. and Leemaqz, Kaleb L. (2021). Multi‐node expectation–maximization algorithm for finite mixture models. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14 (4) sam.11529, 297-304. doi: 10.1002/sam.11529 |
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2021 Journal Article Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errorsFarzammehr, M. A., Mohammadzadeh, M, Zadkarami, M. R. and McLachlan, G. J. (2021). Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors. Communications in Statistics: Theory and Methods, 51 (24), 1-22. doi: 10.1080/03610926.2021.1897843 |
