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December 2011 Model based clustering for three-way data structures
Cinzia Viroli
Bayesian Anal. 6(4): 573-602 (December 2011). DOI: 10.1214/11-BA622

Abstract

The technological progress of the last decades has made a huge amount of information available, often expressed in unconventional formats. Among these, three-way data occur in different application domains from the simultaneous observation of various attributes on a set of units in different situations or locations. These include data coming from longitudinal studies of multiple responses, spatio-temporal data or data collecting multivariate repeated measures. In this work we propose model based clustering for the wide class of continuous three-way data by a general mixture model which can be adapted to the different kinds of three-way data. In so doing we also provide a tool for simultaneously performing model estimation and model selection. The effectiveness of the proposed method is illustrated on a simulation study and on real examples.

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Cinzia Viroli. "Model based clustering for three-way data structures." Bayesian Anal. 6 (4) 573 - 602, December 2011. https://doi.org/10.1214/11-BA622

Information

Published: December 2011
First available in Project Euclid: 13 June 2012

zbMATH: 1330.62262
MathSciNet: MR2869958
Digital Object Identifier: 10.1214/11-BA622

Keywords: birth and death process , Matrix-variate normal distribution , Mixture models , Three-way data

Rights: Copyright © 2011 International Society for Bayesian Analysis

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Vol.6 • No. 4 • December 2011
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