Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	邹国华,首都师范大学
Inviter:
Title:	Asymptotic distribution theory for model averaging based on information criterion
Time & Venue:	2022.10.27 09:40-10:20 腾讯会议号：260233734（密码：220168）翠湖宾馆双翠厅
Abstract:	Smoothed AIC (S-AIC) and Smoothed BIC (S-BIC) are widely used in model averaging. Unlike most other model averaging methods, which depend on the specific model structures, S-AIC and S-BIC can be applied to all situations where AIC and BIC can be calculated, and so are very convenient to implement. However, their theoretical property has not been explored yet. In this paper, we aim to fill this gap. We will study the asymptotic behavior of the S-AIC and S-BIC estimators. A significant difference from the literature, where the local misspecification assumption is usually needed, is that we derive the limiting distributions of the S-AIC and S-BIC weights and estimators under framework of fixed parameter. Based on our limiting theory, we investigate the confidence intervals constructed by Burnham and Anderson (2002), who claimed but did not prove that the resultant coverage probabilities would be close to the intended level. Further, under fixed parameter setup, we provide the confidence interval construction methods for the parameters of interest. Both simulation study and real data analysis support our theoretical conclusions.