Geoffrey J. McLachlan

Computer Science · Physical Sciences

66h-index43.5kcitations486works0.52yr avg
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Research Topics

Bayesian Methods and Mixture Models(230), Statistical Methods and Bayesian Inference(107), Statistical Methods and Inference(98), Advanced Statistical Methods and Models(93), Gene expression and cancer classification(79)

Publications483 total

Informative missingness and its implications in semi-supervised learning
The Innovation Informatics·2026Open Access
Variable selection in frailty mixture cure models via penalized likelihood estimation
arXiv (Cornell University)·2025Open Access
Variable selection in frailty mixture cure models via penalized likelihood estimation
ArXiv.org·2025Open Access
SSLfmm: Semi-Supervised Learning under a Mixed-Missingness Mechanism in Finite Mixture Models
2025Open Access
SSLfmm: An R Package for Semi-Supervised Learning with a Mixed-Missingness Mechanism in Finite Mixture Models
arXiv (Cornell University)·2025Open Access
Pancreaticopleural fistula presenting with a massive right-sided pleural effusion after pancreatitis
˜The œLancet. Gastroenterology & hepatology·2024
Semi‐supervised Gaussian mixture modelling with a missing‐data mechanism in R
Australian & New Zealand Journal of Statistics·2024Open Access
Functional mixtures-of-experts
Statistics and Computing·2024· 5 citedOpen Access
Three Years of EAAs Young Acousticians Network in Social Media
2024Open Access
Effects of Monaural Spectral Cues on Sound Localisation during Small Head Movements
2024Open Access
An overview of skew distributions in model-based clustering: speed presentation April 2022
Science Talks·2024Open Access
A mineralogy characterisation technique for copper ore in flotation pulp using deep learning machine vision with optical microscopy
Minerals Engineering·2023· 10 citedOpen Access
Semi-supervised Gaussian mixture modelling with a missing-data mechanism in R
arXiv (Cornell University)·2023Open Access
An Automated Machine learning (AutoML) approach to regression models in minerals processing with case studies of developing industrial comminution and flotation models
Minerals Engineering·2022· 8 cited
Order selection with confidence for finite mixture models
Journal of the Korean Statistical Society·2022· 2 citedOpen Access
A new algorithm for support vector regression with automatic selection of hyperparameters
Pattern Recognition·2022· 53 cited
A spatial heterogeneity mixed model with skew-elliptical distributions
Communications for Statistical Applications and Methods·2022Open Access
Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
Communication in Statistics- Theory and Methods·2022· 13 citedOpen Access
Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review
Econometrics and Statistics·2022· 14 cited
EM Algorithm
Wiley StatsRef: Statistics Reference Online·2022· 3 citedOpen Access
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Frequent Co-authors

David Peel(8), Sharon Lee(5), K. E. Basford(4), Richard Bean(4), Shu‐Kay Ng(3), Thriyambakam Krishnan(3), L. Ben-Tovim Jones(3), Mark F. O’Brien(3), David C. McGiffin(3), E. Gregory Stafford(3), M. Gardner(3), David J. Hand(2), Douglas M. Hawkins(2), Christophe Ambroise(2), Saumyadipta Pyne(2), Xinli Hu(2), Kui Wang(2), Elizabeth J. Rossin(2), Tsung‐I Lin(2), Lisa M. Maier(2)