Malgorzata Bogdan
Professor
Modified versions of the Bayesian Information Criterion for sparse Generalized Linear Models
Författare
Summary, in English
The classical model selection criteria, such as the Bayesian Information Criterion (BIC) or Akaike information criterion (AIC), have a strong tendency to overestimate the number of regressors when the search is performed over a large number of potential explanatory variables. To handle the problem of the overestimation, several modifications of the BIC have been proposed. These versions rely on supplementing the original BIC with some prior distributions on the class of possible models. Three such modifications are presented and compared in the context of sparse Generalized Linear Models (GLMs). The related choices of priors are discussed and the conditions for the asymptotic equivalence of these criteria are provided. The performance of the modified versions of the BIC is illustrated with an extensive simulation study and a real data analysis. Also, simplified versions of the modified BIC, based on least squares regression, are investigated.
Publiceringsår
2011-11-01
Språk
Engelska
Sidor
2908-2924
Publikation/Tidskrift/Serie
Computational Statistics and Data Analysis
Volym
55
Issue
11
Dokumenttyp
Artikel i tidskrift
Förlag
Elsevier
Ämne
- Probability Theory and Statistics
Nyckelord
- Bayesian Information Criterion
- Generalized Linear Models
- Model selection
- Sparse linear models
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0167-9473