A MODEL SELECTION APPROACH IN STATISTICAL MODELING


Mentes T.

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, vol.39, no.1, pp.131-135, 2010 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 39 Issue: 1
  • Publication Date: 2010
  • Title of Journal : HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS
  • Page Numbers: pp.131-135

Abstract

It is argued that quantitative results from statistical surveys and experiments should be communicated as inferences of the model maximising the log Bayes factor against a reference model penalised by a subjectively chosen constant times the difference in model complexity. Model complexity is measured by the degrees of freedom. In this study, an efficient algorithm is proposed to select a model from among a large set of models with unit penalties in some interval. The algorithm utilizes the penalised log Bayes factor with only the likelihood ratio statistic, model dimensions and a constant. This approach seems to be a more realistic screening device than related criteria similar to the Bayesian information criterion.