Non nested model selection for spatial count regression models with application to health insurance. 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.
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References in zbMATH (referenced in 6 articles , 1 standard article )
Showing results 1 to 6 of 6.
- Duncan Lee; Alastair Rushworth; Gary Napier: Spatio-Temporal Areal Unit Modeling in R with Conditional Autoregressive Priors Using the CARBayesST Package (2018) not zbMATH
- Sáez-Castillo, Antonio J.; Conde-Sánchez, Antonio: Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model (2017)
- Shi, Peng; Shi, Kun: Territorial risk classification using spatially dependent frequency-severity models (2017)
- Czado, Claudia; Schabenberger, Holger; Erhardt, Vinzenz: Non nested model selection for spatial count regression models with application to health insurance (2014)
- Duncan Lee: CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors (2013) not zbMATH
- Almeida, Carlos; Czado, Claudia: Efficient Bayesian inference for stochastic time-varying copula models (2012)