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

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• 作者：Claudia Czado (1)
  Holger Schabenberger (1)
  Vinzenz Erhardt (1)
  
• 关键词：Spatial count regression ; Over ; dispersion ; Zero ; inflation ; Generalized Poisson ; Non nested comparison
• 刊名：Statistical Papers
• 出版年：2014
• 出版时间：May 2014
• 年：2014
• 卷：55
• 期：2
• 页码：455-476
• 全文大小：2,481 KB
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• 作者单位：Claudia Czado (1)
  Holger Schabenberger (1)
  Vinzenz Erhardt (1)
  
  1. Technische Universit?t München, Zentrum Mathematik, Lehrstuhl für Mathematische Statistik, Boltzmannstr. 3, 85748, Garching, Germany
  
• ISSN：1613-9798

文摘

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.