Estimation and selection for the latent block model on categorical data

Abstract : This paper deals with estimation and model selection in the Latent Block Model (LBM) for categorical data. First, after providing sufficient conditions ensuring the identifiability of this model, we generalise estimation procedures and model selection criteria derived for binary data. Secondly, we develop Bayesian inference through Gibbs sampling and with a well calibrated non informative prior distribution, in order to get the MAP estimator: this is proved to avoid the traps encountered by the LBM with the maximum likelihood methodology. Then model selection criteria are presented. In particular an exact expression of the integrated completed likelihood criterion requiring no asymptotic approximation is derived. Finally numerical experiments on both simulated and real data sets highlight the appeal of the proposed estimation and model selection procedures.
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Article dans une revue
Statistics and Computing, Springer Verlag (Germany), 2015, 25 (6), pp.1201-1216. 〈10.1007/s11222-014-9472-2〉
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https://hal.inria.fr/hal-01256840
Contributeur : Gilles Celeux <>
Soumis le : vendredi 15 janvier 2016 - 13:49:56
Dernière modification le : jeudi 11 janvier 2018 - 06:26:37

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Christine Keribin, Vincent Brault, Gilles Celeux, Gérard Govaert. Estimation and selection for the latent block model on categorical data. Statistics and Computing, Springer Verlag (Germany), 2015, 25 (6), pp.1201-1216. 〈10.1007/s11222-014-9472-2〉. 〈hal-01256840〉

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