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|Title:||Variable selection and model averaging in semiparametric overdispersed generalized linear models|
Markov chain Monte Carlo
|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|
|Appears in Collections:||Staff Publications|
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