Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models

Simple item page
dc.creatorCottet, Remy
dc.creatorKohn, Robert
dc.creatorNott, David
dc.date2007-07-14
dc.date.accessioned2026-07-07T08:18:27Z
dc.date.available2026-07-07T08:18:27Z
dc.descriptionWe 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.
dc.description8 graphs 35 pages
dc.identifierhttps://arxiv.org/abs/0707.2158
dc.identifierhttp://arxiv.org/abs/0707.2158
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/134435
dc.subjectMethodology
dc.titleVariable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
dc.typetext

Files

Collections

arXiv