192 citations · 426 across the 48 of their papers we have counts for
4 papers · 2 filters
The skew-t factor analysis model
Tsung-I Lin, Pal H. Wu, Geoffrey J. McLachlan +1
Factor analysis is a classical data reduction technique that seeks a potentially lower number of unobserved variables that can account for the correlations among the observed varia…
Inference on differences between classes using cluster-specific contrasts of mixed effects
S. K. Ng, G. J. McLachlan, K. Wang +3
The detection of differentially expressed (DE) genes is one of the most commonly studied problems in bioinformatics. For example, the identification of DE genes between distinct di…
Extending mixtures of factor models using the restricted multivariate skew-normal distribution
Tsung-I Lin, Geoffrey J. McLachlan, Sharon X. Lee
The mixture of factor analyzers (MFA) model provides a powerful tool for analyzing high-dimensional data as it can reduce the number of free parameters through its factor-analytic…
Mixtures of Spatial Spline Regressions
Hien D. Nguyen, Geoffrey J. McLachlan, Ian A. Wood
We present an extension of the functional data analysis framework for univariate functions to the analysis of surfaces: functions of two variables. The spatial spline regression (S…