Robust mixture modeling using the skew t distribution

Authors:
Tsung I. Lin;Jack C. Lee;Wan J. Hsieh
Affiliations:
Department of Applied-Mathematics, National Chung Hsing University, Taichung, Taiwan;Graduate Institute of Finance, National Chiao Tung University, Hsinchu, Taiwan;Institute of Statistics, National Chiao Tung University, Hsinchu, Taiwan
Venue:
Statistics and Computing
Year:
2007

Citing 5
Cited 14

Robust mixture modelling using the t distribution

Statistics and Computing
Bayesian analysis of mixture modelling using the multivariate t distribution

Statistics and Computing
Robust mixture modelling using multivariate t-distribution with missing information

Pattern Recognition Letters
Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm

Statistics and Computing
Multivariate mixtures of normals with unknown number of components

Statistics and Computing

Bayesian density estimation using skew student-t-normal mixtures

Computational Statistics & Data Analysis
Maximum likelihood estimation for multivariate skew normal mixture models

Journal of Multivariate Analysis
Model-based clustering with non-elliptically contoured distributions

Statistics and Computing
A robust Bayesian approach to null intercept measurement error model with application to dental data

Computational Statistics & Data Analysis
Robust mixture modeling using multivariate skew t distributions

Statistics and Computing
Robust mixture modeling based on scale mixtures of skew-normal distributions

Computational Statistics & Data Analysis
Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

Computational Statistics & Data Analysis
Maximum likelihood inference for mixtures of skew Student-t-normal distributions through practical EM-type algorithms

Statistics and Computing
Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution

Statistics and Computing
Multivariate mixture modeling using skew-normal independent distributions

Computational Statistics & Data Analysis
On mixtures of skew normal and skew $$t$$-distributions

Advances in Data Analysis and Classification
Using conditional independence for parsimonious model-based Gaussian clustering

Statistics and Computing
Model-based clustering of high-dimensional data: A review

Computational Statistics & Data Analysis
Finite mixtures of multivariate skew t-distributions: some recent and new results

Statistics and Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A finite mixture model using the Student's t distribution has been recognized as a robust extension of normal mixtures. Recently, a mixture of skew normal distributions has been found to be effective in the treatment of heterogeneous data involving asymmetric behaviors across subclasses. In this article, we propose a robust mixture framework based on the skew t distribution to efficiently deal with heavy-tailedness, extra skewness and multimodality in a wide range of settings. Statistical mixture modeling based on normal, Student's t and skew normal distributions can be viewed as special cases of the skew t mixture model. We present analytically simple EM-type algorithms for iteratively computing maximum likelihood estimates. The proposed methodology is illustrated by analyzing a real data example.