Abstract.
This article considers the problem of post-averaging inference for optimal model averaging estimators in a generalized linear model (GLM). We establish the asymptotic distributions of optimal model averaging estimators for GLMs. The asymptotic distributions of the model averaging estimators are nonstandard, depending on the configuration of the penalty term in the weight choice criterion. We also propose a feasible simulation-based confidence interval estimator and investigate its asymptotic properties rigorously. Monte Carlo simulations verify the usefulness of our theoretical results, and the proposed methods are employed to analyze a stock car racing dataset.
Acknowledgments
The authors would like to thank the Editor Prof. Esfandiar Maasoumi, an associate editor and an anonymous reviewer for their constructive suggestions and comments that have substantially improved earlier version of this article.