Malgorzata Bogdan

Professor

Selecting explanatory variables with the modified version of the bayesian information criterion

Författare

Malgorzata Bogdan
Jayanta K. Ghosh
Małgorzata Zak-Szatkowska

Summary, in English

We consider the situation in which a large database needs to be analyzed to identify a few important predictors of a given quantitative response variable. There is a lot of evidence that in this case classical model selection criteria, such as the Akaike information criterion or the Bayesian information criterion (BIC), have a strong tendency to overestimate the number of regressors. In our earlier papers, we developed the modified version of BIC (mBIC), which enables the incorporation of prior knowledge on a number of regressors and prevents overestimation. In this article, we review earlier results on mBIC and discuss the relationship of this criterion to the well-known Bonferroni correction for multiple testing and the Bayes oracle, which minimizes the expected costs of inference. We use computer simulations and a real data analysis to illustrate the performance of the original mBIC and its rank version, which is designed to deal with data that contain some outlying observations.

Publiceringsår

2008-10

Språk

Engelska

Sidor

627-641

Publikation/Tidskrift/Serie

Quality and Reliability Engineering International

Volym

Issue

Länkar

Dokumenttyp