Cottet, Remy; Kohn, Robert J.; Nott, David J. Variable selection and model averaging in semiparametric overdispersed generalized linear models. (English) Zbl 1469.62311 J. Am. Stat. Assoc. 103, No. 482, 661-671 (2008). Summary: We express the mean and variance terms in a double-exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predictors enter the model and whether they enter linearly or flexibly. When the variance term is null, we obtain a generalized additive model, which becomes a generalized linear model if the predictors enter the mean linearly. The model is estimated using Markov chain Monte Carlo simulation, and the methodology is illustrated using real and simulated data sets. Cited in 10 Documents MSC: 62J12 Generalized linear models (logistic models) 62F15 Bayesian inference Keywords:Bayesian analysis; double-exponential family; hierarchical prior; Markov chain Monte Carlo Software:SemiPar PDFBibTeX XMLCite \textit{R. Cottet} et al., J. Am. Stat. Assoc. 103, No. 482, 661--671 (2008; Zbl 1469.62311) Full Text: DOI arXiv