Name
Title | ||
|---|---|---|
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution |
Abstract | ||
|---|---|---|
Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensional data, where the number of observations n is small relative to their dimension p. However, this approach is sensitive to outliers as it is based on a mixture model in which the multivariate normal family of distributions is assumed for the component error and factor distributions. An extension to mixtures of t-factor analyzers is considered, whereby the multivariate t-family is adopted for the component error and factor distributions. An EM-based algorithm is developed for the fitting of mixtures of t-factor analyzers. Its application is demonstrated in the clustering of some microarray gene-expression data. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.csda.2006.09.015 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
factor distribution,factor analyzers model,dimension p,high-dimensional data,multivariate t-family,microarray gene-expression data,component error,multivariate normal family,t-factor analyzer,em-based algorithm,factor analyzer,multivariate t-distribution,em algorithm,multivariate normal,density estimation,high dimensional data,multivariate t distribution,mixture model,computer science | Multivariate t-distribution,Econometrics,Density estimation,Multivariate statistics,Expectation–maximization algorithm,Outlier,Multivariate normal distribution,Statistics,Cluster analysis,Mathematics,Mixture model | Journal |
Volume | Issue | ISSN |
51 | 11 | Computational Statistics and Data Analysis |
Citations | PageRank | References |
48 | 2.62 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| G. J. McLachlan | 1 | 469 | 49.38 |
| Richard Bean | 2 | 369 | 33.05 |
| L. Ben-Tovim Jones | 3 | 142 | 9.50 |