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Advances in Statistical Models for Data AnalysisFamilies of Parsimonious Finite Mixtures of Regression Models

Advances in Statistical Models for Data Analysis: Families of Parsimonious Finite Mixtures of... [Finite mixtures of regression (FMR) models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate error covariance matrix. By constraining parts of this decomposition, we obtain families of parsimonious mixtures of regressions and mixtures of regressions with concomitant variables. These families of models account for correlations between multiple responses. An expectation-maximization algorithm is presented for parameter estimation and performance is illustrated on simulated and real data.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Advances in Statistical Models for Data AnalysisFamilies of Parsimonious Finite Mixtures of Regression Models

Editors: Morlini, Isabella; Minerva, Tommaso; Vichi, Maurizio

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References (23)

Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2015
ISBN
978-3-319-17376-4
Pages
73 –84
DOI
10.1007/978-3-319-17377-1_9
Publisher site
See Chapter on Publisher Site

Abstract

[Finite mixtures of regression (FMR) models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate error covariance matrix. By constraining parts of this decomposition, we obtain families of parsimonious mixtures of regressions and mixtures of regressions with concomitant variables. These families of models account for correlations between multiple responses. An expectation-maximization algorithm is presented for parameter estimation and performance is illustrated on simulated and real data.]

Published: May 14, 2015

Keywords: Concomitant variables; EM algorithm; Finite mixtures of regressions; Mixture models; Multivariate response

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