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Bayesian multiple comparison of models for binary data with inequality constraints

Title
Bayesian multiple comparison of models for binary data with inequality constraints
Authors
Oh M.-S.
Ewha Authors
오만숙
SCOPUS Author ID
오만숙scopus
Issue Date
2013
Journal Title
Statistics and Computing
ISSN
0960-3174JCR Link
Citation
vol. 23, no. 4, pp. 481 - 490
Indexed
SCIE; SCOPUS WOS scopus
Abstract
In this paper we consider generalized linear models for binary data subject to inequality constraints on the regression coefficients, and propose a simple and efficient Bayesian method for parameter estimation and model selection by using Markov chain Monte Carlo (MCMC). In implementing MCMC, we introduce appropriate latent variables and use a simple approximation of a link function, to resolve computational difficulties and obtain convenient forms for full conditional posterior densities of elements of parameters. Bayes factors are computed via the Savage-Dickey density ratios and the method of Oh (Comput. Stat. Data Anal. 29:411-427, 1999), for which posterior samples from the full model with no degenerate parameter and the full conditional posterior densities of elements are needed. Since it uses one set of posterior samples from the full model for any model in consideration, it performs simultaneous comparison of all possible models and is very efficient compared with other model selection methods which require one to fit all candidate models.A simulation study shows that significant improvements can be made by taking the constraints into account. Real data on purchase intention of a product subject to order constraints is analyzed by using the proposed method. The analysis results show that there exist some price changes which significantly affect the consumer behavior. The results also show the importance of simultaneous comparison of models rather than separate pairwise comparisons of models since the latter may yield misleading results from ignoring possible correlations between parameters. © 2012 Springer Science+Business Media, LLC.
DOI
10.1007/s11222-012-9324-x
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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