Optimal model averaging for multivariate regression models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Article number | 104858 |
Journal / Publication | Journal of Multivariate Analysis |
Volume | 189 |
Online published | 8 Nov 2021 |
Publication status | Published - May 2022 |
Link(s)
Abstract
In this paper, frequentist model averaging is considered in the context of a multivariate multiple regression model. We propose a weight choice criterion based on a plug-in counterpart of the quadratic risk of the model average estimator that involves an approximation of the distribution of a ratio of quadratic forms by an F distribution. We establish an asymptotic theory for the resultant model average estimator for both the general and restricted weight sets, and derive the convergence rate of the model weights to the quadratic risk-based optimal weights. The merits of our approach are illustrated by a simulation study and an application based on from the Sixth National Population Census of China.
Research Area(s)
- Asymptotic optimality, Consistency, F distribution, Model averaging, Weight choice
Citation Format(s)
Optimal model averaging for multivariate regression models. / Liao, Jun; Wan, Alan T.K.; He, Shuyuan et al.
In: Journal of Multivariate Analysis, Vol. 189, 104858, 05.2022.
In: Journal of Multivariate Analysis, Vol. 189, 104858, 05.2022.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review