Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data

Francesco Bartolucci, Luisa Scaccia and Alessio Farcomeni

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4067-4080

Abstract: A Bayesian approach is developed for selecting the model that is most supported by the data within a class of marginal models for categorical variables, which are formulated through equality and/or inequality constraints on generalized logits (local, global, continuation, or reverse continuation), generalized log-odds ratios, and similar higher-order interactions. For each constrained model, the prior distribution of the model parameters is specified following the encompassing prior approach. Then, model selection is performed by using Bayes factors estimated through an importance sampling method. The approach is illustrated by three applications based on different datasets, which also include explanatory variables. In connection with one of these examples, a sensitivity analysis to the prior specification is also performed.

Keywords: Bayes factor; Generalized logits; Inequality constraints; Marginal likelihood; Positive association (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4067-4080

DOI: 10.1016/j.csda.2012.04.006

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