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Research Article

Post-averaging inference for optimal model averaging estimator in generalized linear models

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Pages 98-122 | Published online: 03 Jan 2024
 

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.

Additional information

Funding

All remaining errors are solely ours. Dalei Yu was supported by National Natural Science Foundation of China (NNSFC, Grant Nos. 12071414 and 11661079); Yuying Sun was supported by NNSFC (Grant Nos. 72322016, 72073126, 71973116 and 72091212) and Young Elite Scientists Sponsorship Program by CAST; Xinyu Zhang was supported by NNSFC (Grant Nos. 71925007, 72091212 and 12288201) and the CAS Project for Young Scientists in Basic Research (YSBR-008), and Yongmiao Hong was supported by NNSFC Fundamental Science Center Project (Grant No. 71988101) entitled as Econometric Modeling and Economic Policy Studies.

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