Skip to main content

Model selection principles in misspecified models

Buy Article:

$43.00 plus tax (Refund Policy)

Model selection is of fundamental importance to high dimensional modelling featured in many contemporary applications. Classical principles of model selection include the Bayesian principle and the Kullback–Leibler divergence principle, which lead to the Bayesian information criterion and Akaike information criterion respectively, when models are correctly specified. Yet model misspecification is unavoidable in practice. We derive novel asymptotic expansions of the two well‐known principles in misspecified generalized linear models, which give the generalized Bayesian information criterion and generalized Akaike information criterion. A specific form of prior probabilities motivated by the Kullback–Leibler divergence principle leads to the generalized Bayesian information criterion with prior probability, GBICp, which can be naturally decomposed as the sum of 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 new methods for model selection in both correctly specified and misspecified models.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Keywords: Akaike information criterion; Bayesian information criterion; Bayesian principle; Generalized Akaike information criterion; Generalized Bayesian information criterion; Generalized Bayesian information criterion with prior probability; KullbackÔÇôLeibler divergence principle; Model misspecification; Model selection

Document Type: Research Article

Publication date: 2014-01-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more