Translator Disclaimer
2013 Generalized predictive information criteria for the analysis of feature events
Mike K. P. So, Tomohiro Ando
Electron. J. Statist. 7: 742-762 (2013). DOI: 10.1214/13-EJS788

Abstract

This paper develops two weighted measures for model selection by generalizing the Kullback-Leibler divergence measure. The concept of a model selection process that takes into account the special features of the underlying model is introduced using weighted measures. New information criteria are defined using the bias correction of an expected weighted loglikelihood estimator. Using weight functions that match the features of interest in the underlying statistical models, the new information criteria are applied to simulated studies of spline regression and copula model selection. Real data applications are also given for predicting the incidence of disease and for quantile modeling of environmental data.

Citation

Download Citation

Mike K. P. So. Tomohiro Ando. "Generalized predictive information criteria for the analysis of feature events." Electron. J. Statist. 7 742 - 762, 2013. https://doi.org/10.1214/13-EJS788

Information

Published: 2013
First available in Project Euclid: 25 March 2013

zbMATH: 1336.62041
MathSciNet: MR3040558
Digital Object Identifier: 10.1214/13-EJS788

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

JOURNAL ARTICLE
21 PAGES


SHARE
Back to Top