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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 |
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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 Conference Publication Extending FaultSeg3D to Minerals Seismic: Part 1 – A synthetic 3D-seismic training-volume generator for preparing data replicating a hardrock terrane to train an automatic-fault-prediction algorithmChatterjee, Robindra , Valenta, Richard , McLachlan, Geoffrey and Weatherley, Dion (2021). Extending FaultSeg3D to Minerals Seismic: Part 1 – A synthetic 3D-seismic training-volume generator for preparing data replicating a hardrock terrane to train an automatic-fault-prediction algorithm. Australian Earth Science Convention, Virtual, 9-12 February 2021. |
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2021 Book Chapter Estimation of classification rules from partially classified dataMcLachlan, Geoffrey and Ahfock, Daniel (2021). Estimation of classification rules from partially classified data. Data analysis and rationality in a complex world. (pp. 149-157) edited by Theodore Chadjipadelis, Berthold Lausen, Angelos Markos, Tae Rim Lee, Angela Montanari and Rebecca Nugent. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-60104-1_17 |
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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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2021 Conference Publication On Mean And/or Variance Mixtures of Normal DistributionsLee, Sharon X. and McLachlan, Geoffrey J. (2021). On Mean And/or Variance Mixtures of Normal Distributions. 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), Cassino, Italy, 11–13 September 2019. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-69944-4_13 |
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2021 Book Chapter Automated gating and dimension reduction of high-dimensional cytometry dataLee, Sharon X., McLachlan, Geoffrey J. and Pyne, Saumyadipta (2021). Automated gating and dimension reduction of high-dimensional cytometry data. Mathematical, computational and experimental T cell immunology. (pp. 281-294) edited by Carmen Molina-París and Grant Lythe . Cham, Switzerland: Springer. doi: 10.1007/978-3-030-57204-4_16 |
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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). 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 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 |
