Please use this identifier to cite or link to this item: https://doi.org/10.1198/016214508000000346
Title: Variable selection and model averaging in semiparametric overdispersed generalized linear models
Authors: Cottet, R.
Kohn, R.J.
Nott, D.J. 
Keywords: Bayesian analysis
Double-exponential family
Hierarchical prior
Markov chain Monte Carlo
Issue Date: Jun-2008
Citation: Cottet, R., Kohn, R.J., Nott, D.J. (2008-06). Variable selection and model averaging in semiparametric overdispersed generalized linear models. Journal of the American Statistical Association 103 (482) : 661-671. ScholarBank@NUS Repository. https://doi.org/10.1198/016214508000000346
Abstract: 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. © 2008 American Statistical Association.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105458
ISSN: 01621459
DOI: 10.1198/016214508000000346
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