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

DOI10.1007/s00362-012-0491-9zbMath1297.62123OpenAlexW2041006547MaRDI QIDQ744767

Vinzenz Erhardt, Holger Schabenberger, Claudia Czado

Publication date: 26 September 2014

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-012-0491-9

zbMATH Keywords

zero-inflationover-dispersiongeneralized Poissonnon nested comparisonspatial count regression

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Linear regression; mixed models (62J05) Applications of statistics to actuarial sciences and financial mathematics (62P05)

Related Items

TERRITORIAL RISK CLASSIFICATION USING SPATIALLY DEPENDENT FREQUENCY-SEVERITY MODELS, Efficient Bayesian inference for stochastic time-varying copula models, Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model, Count Distribution for Generalized Weibull Duration with Applications, spatcounts

Uses Software

• R
• CRAN
• BayesDA
• ZIGP

Cites Work

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