Robust Mixture Modelling Using the t Distribution

Peel, D. and McLachlan, G. J. (2000) Robust Mixture Modelling Using the t Distribution. Statistics and Computing, 10 4: 339-348. doi:10.1023/A:1008981510081


Author Peel, D.
McLachlan, G. J.
Title Robust Mixture Modelling Using the t Distribution
Journal name Statistics and Computing   Check publisher's open access policy
ISSN 0960-3174
Publication date 2000-10-01
Year available 2000
Sub-type Article (original research)
DOI 10.1023/A:1008981510081
Open Access Status Not yet assessed
Volume 10
Issue 4
Start page 339
End page 348
Total pages 10
Editor Hand, D.
Place of publication Dordrecht, Netherlands
Publisher Kluwer
Language eng
Subject C1
780101 Mathematical sciences
0104 Statistics
Abstract Normal mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster sets of continuous multivariate data. However, for a set of data containing a group or groups of observations with longer than normal tails or atypical observations, the use of normal components may unduly affect the fit of the mixture model. In this paper, we consider a more robust approach by modelling the data by a mixture of t distributions. The use of the ECM algorithm to fit this t mixture model is described and examples of its use are given in the context of clustering multivariate data in the presence of atypical observations in the form of background noise.
Keyword Computer Science, Theory & Methods
Statistics & Probability
Finite Mixture Models
Normal Components
Multivariate T Components
Maximum Likelihood
Em Algorithm
Cluster Analysis
Em Algorithm
Maximum-likelihood
Ecme Algorithm
Ml-estimation
Convergence
Q-Index Code C1
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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Created: Mon, 13 Aug 2007, 21:57:01 EST