Bayesian Analysis

Model based clustering for three-way data structures

Cinzia Viroli

Full-text: Open access

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.

Article information

Source
Bayesian Anal., Volume 6, Number 4 (2011), 573-602.

Dates
First available in Project Euclid: 13 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1339616537

Digital Object Identifier
doi:10.1214/11-BA622

Mathematical Reviews number (MathSciNet)
MR2869958

Zentralblatt MATH identifier
1330.62262

Keywords
Mixture models Birth and death process Matrix-variate normal distribution Three-way data

Citation

Viroli, Cinzia. Model based clustering for three-way data structures. Bayesian Anal. 6 (2011), no. 4, 573--602. doi:10.1214/11-BA622. https://projecteuclid.org/euclid.ba/1339616537


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