192 citations · 426 across the 48 of their papers we have counts for
5 papers · 1 filter
On formulations of skew factor models: skew errors versus skew factors
Sharon X. Lee, Geoffrey J. McLachlan
In the past few years, there have been a number of proposals for generalizing the factor analysis (FA) model and its mixture version (known as mixtures of factor analyzers (MFA)) u…
False discovery rate control under reduced precision computation for analysis of neuroimaging data
Hien D. Nguyen, Yohan Yee, Geoffrey J. McLachlan +1
The mitigation of false positives is an important issue when conducting multiple hypothesis testing. The most popular paradigm for false positives mitigation in high-dimensional ap…
Randomized Mixture Models for Probability Density Approximation and Estimation
Hien D. Nguyen, Dianhui Wang, Geoffrey J. McLachlan
Randomized neural networks (NNs) are an interesting alternative to conventional NNs that are more used for data modeling. The random vector functional-link (RVFL) network is an est…
Positive data kernel density estimation via the logKDE package for R
Andrew T. Jones, Hien D. Nguyen, Geoffrey J. McLachlan
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric estimation of probability density functions (PDFs), when data are obtained from unknown data generating proc…
Mixtures of Factor Analyzers with Fundamental Skew Symmetric Distributions
Sharon X. Lee, Tsung-I Lin, Geoffrey J. McLachlan
Mixtures of factor analyzers (MFA) provide a powerful tool for modelling high-dimensional datasets. In recent years, several generalizations of MFA have been developed where the no…