7 citations · 20 across the 5 of their papers we have counts for
5 papers
Scalable Bayesian Spatiotemporal Tensor Modeling for Censored and Missing Areal Data
Jose A. Ordoñez, Tsung-I Lin, Victor H. Lachos +1
We propose a new Bayesian approach for spatiotemporal areal data with censored and missing observations, where the response is represented as a tensor indexed by spatial units and…
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…
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…
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…
Capturing Patterns via Parsimonious t Mixture Models
Tsung-I Lin, Paul D. McNicholas, Hsiu J. Ho
This paper exploits a simplified version of the mixture of multivariate t-factor analyzers (MtFA) for robust mixture modelling and clustering of high-dimensional data that frequent…