Comparison of Bayesian objective procedures for variable selection in linear regression

• Autores: Elías Moreno Bas, Francisco Javier Girón González-Torre
• Localización: Test: An Official Journal of the Spanish Society of Statistics and Operations Research, ISSN-e 1863-8260, ISSN 1133-0686, Vol. 17, Nº. 3, 2008, págs. 472-490
• Idioma: inglés
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• Resumen

  • In the objective Bayesian approach to variable selection in regression a crucial point is the encompassing of the underlying nonnested linear models. Once the models have been encompassed, one can define objective priors for the multiple testing problem involved in the variable selection problem.

    There are two natural ways of encompassing: one way is to encompass all models into the model containing all possible regressors, and the other is to encompass the model containing only the intercept into any other.

    In this paper we compare the variable selection procedures that result from each of the two mentioned ways of encompassing by analysing their theoretical properties and their behavior with simulated and real data.

    Relations with frequentest criteria for model selection, such as those based on the R adj 2 and Mallows C p , are provided incidentally.