Optimal model averaging for multivariate regression models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number104858
Journal / PublicationJournal of Multivariate Analysis
Volume189
Online published8 Nov 2021
Publication statusPublished - May 2022

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