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• W2949204313 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." @default.
• W2949204313 created "2019-06-27" @default.
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• W2949204313 date "2007-07-14" @default.
• W2949204313 modified "2023-09-27" @default.
• W2949204313 title "Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models" @default.
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