Finite Mixture Models
- G. McLachlan, D. Peel
- Computer ScienceWiley Series in Probability and Statistics
- 2 October 2000
The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models.
Robust mixture modelling using the t distribution
- D. Peel, G. McLachlan
- Mathematics, Computer ScienceStatistics and computing
- 1 October 2000
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.
A mixture model-based approach to the clustering of microarray expression data
- G. McLachlan, Richard Bean, D. Peel
- Computer ScienceBioinform.
- 1 March 2002
The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues, and relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classified tissues or with background and biological knowledge of these sets.
The EMMIX software for the fitting of mixtures of normal and t-components
- G. McLachlan, D. Peel, K. Basford, Peter Adams
- Mathematics, Computer Science
- 1999
An algorithm called EMMIX is described that automatically undertakes the fitting of normal or t-component mixture models to multivariate data, using maximum likelihood via the EM algorithm, including the provision of suitable initial values if not supplied by the user.
Modelling high-dimensional data by mixtures of factor analyzers
- G. McLachlan, D. Peel, Richard Bean
- Computer ScienceComputational Statistics & Data Analysis
- 28 January 2003
Mixtures of Factor Analyzers
- G. McLachlan, D. Peel
- Computer ScienceInternational Conference on Machine Learning
- 29 June 2000
Robust Cluster Analysis via Mixtures of Multivariate t-Distributions
- G. McLachlan, D. Peel
- Mathematics, Computer ScienceSSPR/SPR
- 11 August 1998
The expectation-maximization (EM) algorithm can be used to fit mixtures of multivariate t-distributions by maximum likelihood and it is demonstrated how the use of t-components provides less extreme estimates of the posterior probabilities of cluster membership.
Fitting Mixtures of Kent Distributions to Aid in Joint Set Identification
- D. Peel, W. J. Whiten, G. McLachlan
- Geology
- 1 March 2001
When examining a rock mass, joint sets and their orientations can play a significant role with regard to how the rock mass will behave. To identify joint sets present in the rock mass, the…
Standard errors of fitted component means of normal mixtures
- K. Basford, D. Greenway, G. McLachlan, D. Peel
- Mathematics
- 1997
In this paper use consider the problem of providing standard errors of the component means in normal mixture models fitted to univariate or multivariate data by maximum likelihood via the EM…
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