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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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2022 Journal Article Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errorsFarzammehr, Mohadeseh Alsadat, Mohammadzadeh, Mohsen, Zadkarami, Mohammad Reza and McLachlan, Geoffrey J. (2022). Bayesian analysis of generalized linear mixed models with spatial correlated and unrestricted skew normal errors. Communications in Statistics: Theory and Methods, 51 (24), 8476-8498. doi: 10.1080/03610926.2021.1897843 |
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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 Harmless label noise and informative soft-labels in supervised classificationAhfock, Daniel and McLachlan, Geoffrey J. (2021). Harmless label noise and informative soft-labels in supervised classification. Computational Statistics and Data Analysis, 161 107253, 107253. doi: 10.1016/j.csda.2021.107253 |
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2021 Journal Article On formulations of skew factor models: Skew factors and/or skew errorsLee, Sharon X. and McLachlan, Geoffrey J. (2021). On formulations of skew factor models: Skew factors and/or skew errors. Statistics and Probability Letters, 168 108935, 108935. doi: 10.1016/j.spl.2020.108935 |
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2020 Journal Article An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classifiedAhfock, Daniel and McLachlan, Geoffrey J. (2020). An apparent paradox: a classifier based on a partially classified sample may have smaller expected error rate than that if the sample were completely classified. Statistics and Computing, 30 (6), 1779-1790. doi: 10.1007/s11222-020-09971-5 |
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2020 Journal Article Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributionsLee, Sharon X., Lin, Tsung-I and McLachlan, Geoffrey J. (2020). Mixtures of factor analyzers with scale mixtures of fundamental skew normal distributions. Advances in Data Analysis and Classification, 15 (2), 481-512. doi: 10.1007/s11634-020-00420-9 |
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2020 Journal Article A Mixture of Regressions Model of COVID-19 Death Rates and Population ComorbiditiesMaleki, M., McLachlan, G. J., Gurewitsch, R., Aruru, M. and Pyne, S. (2020). A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities. Statistics and Applications, 18 (1), 295-306. |
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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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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 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 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 |
