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A finite mixture model for multivariate counts under endogenous selectivity

Alfò, Marco, Maruotti, Antonello and Trovato, Giovanni (2011) A finite mixture model for multivariate counts under endogenous selectivity Statistics and Computing, 21, (2), pp. 185-202.

Record type: Article


We describe a selection model for multivariate counts, where association between the primary outcomes and the endogenous selection source is modeled through outcome-specific latent effects which are assumed to be dependent across equations. Parametric specifications of this model already exist in the literature; in this paper, we show how model parameters can be estimated in a finite mixture context. This approach helps us to consider overdispersed counts, while allowing for multivariate association and endogeneity of the selection variable. In this context, attention is focused both on bias in estimated effects when exogeneity of selection (treatment) variable is assumed, as well as on consistent estimation of the association between the random effects in the primary and in the treatment effect models, when the latter is assumed endogeneous. The model behavior is investigated through a large scale simulation experiment. An empirical example on health care utilization data is provided.

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e-pub ahead of print date: 25 November 2009
Published date: April 2011
Keywords: multivariate counts, selection models, random effects, nonparametric ML, finite mixtures
Organisations: Statistics, Statistical Sciences Research Institute


Local EPrints ID: 341364
ISSN: 0960-3174
PURE UUID: 10492d00-e5b9-4a09-b7c8-6cfc520f6b7e

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Date deposited: 23 Jul 2012 13:42
Last modified: 01 Oct 2017 03:42

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Author: Marco Alfò
Author: Antonello Maruotti
Author: Giovanni Trovato

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