@article{article_531117, title={A MODEL SELECTION APPROACH IN STATISTICAL MODELING}, journal={Hacettepe Journal of Mathematics and Statistics}, volume={39}, pages={131–135}, author={Menteş, Turhan}, keywords={Model selection,Bayes factor,Penalty function,Utility function}, 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.}, number={1}, publisher={Hacettepe University}