Model Selection Principles in Misspecified Models

Details Model Selection Principles in Misspecified Models Model Selection Principles in Misspecified Models

2010-05-29

arxiv.org/abs/1005.5483v1

Mathematics

Statistics Theory

33 pages

Scientific paper

Model selection is of fundamental importance to high dimensional modeling featured in many contemporary applications. Classical principles of model selection include the Kullback-Leibler divergence principle and the Bayesian principle, which lead to the Akaike information criterion and Bayesian information criterion when models are correctly specified. Yet model misspecification is unavoidable when we have no knowledge of the true model or when we have the correct family of distributions but miss some true predictor. In this paper, we propose a family of semi-Bayesian principles for model selection in misspecified models, which combine the strengths of the two well-known principles. We derive asymptotic expansions of the semi-Bayesian principles in misspecified generalized linear models, which give the new semi-Bayesian information criteria (SIC). A specific form of SIC admits a natural decomposition into the negative maximum quasi-log-likelihood, a penalty on model dimensionality, and a penalty on model misspecification directly. Numerical studies demonstrate the advantage of the newly proposed SIC methodology for model selection in both correctly specified and misspecified models.

Affiliated with

Also associated with

No associations

Rating

Model Selection Principles in Misspecified Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Model Selection Principles in Misspecified Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Model Selection Principles in Misspecified Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-161201