Variable selection and model averaging in semiparametric overdispersed generalized linear models

• 成果类型: Article
• 署名作者: Cottet, Remy; Kohn, Robert J.; Nott, David J.
• 署名单位: University of Sydney; University of New South Wales Sydney; National University of Singapore
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000346
• 发表日期: 2008
• 页码: 661-671
• 关键词: extended quasi-likelihood; Nonparametric Regression; mixed models; count data; splines; inference; variance

摘要

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.