A MODEL SELECTION APPROACH IN STATISTICAL MODELING

Year 2010, Volume: 39 Issue: 1, 131 - 135, 03.07.2019

Turhan Menteş

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.

Keywords

Model selection, Bayes factor, Penalty function, Utility function

References

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