Non nested model selection for spatial count regression models with application to health insurance

Claudia Czado (), Holger Schabenberger and Vinzenz Erhardt ()

Statistical Papers, 2014, vol. 55, issue 2, 455-476

Abstract: In this paper we consider spatial regression models for count data. We examine not only the Poisson distribution but also the generalized Poisson capable of modeling over-dispersion, the negative Binomial as well as the zero-inflated Poisson distribution which allows for excess zeros as possible response distribution. We add random spatial effects for modeling spatial dependency and develop and implement MCMC algorithms in $$R$$ for Bayesian estimation. The corresponding R library ‘spatcounts’ is available on CRAN. In an application the presented models are used to analyze the number of benefits received per patient in a German private health insurance company. Since the deviance information criterion is only appropriate for exponential family models, we use in addition the Vuong and Clarke test with a Schwarz correction to compare possibly non nested models. We illustrate how they can be used in a Bayesian context. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Spatial count regression; Over-dispersion; Zero-inflation; Generalized Poisson; Non nested comparison (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00362-012-0491-9

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