This paper considers the model averaging prediction in a quasi-likelihood framework that
allows for parameter uncertainty and model misspecification. We propose an averaging …

Variable selection methods and model selection approaches are valuable statistical tools
that are indispensable for almost any statistical modeling question. This review first …

Abstract Model averaging has become a popular method of estimation, following increasing
evidence that model selection and estimation should be treated as one joint procedure …

Considering model averaging estimation in generalized linear models, we propose a weight
choice criterion based on the Kullback–Leibler (KL) loss with a penalty term. This criterion is …

The past decade witnessed a literature on model averaging by frequentist methods. For the
most part, the asymptotic optimality of various existing frequentist model averaging …

This paper derives the limiting distributions of least squares averaging estimators for linear
regression models in a local asymptotic framework. We show that the averaging estimators …

Abstract Model averaging generally provides better predictions than model selection, but the
existing model averaging methods cannot lead to parsimonious models. Parsimony is an …

This paper develops a frequentist model averaging method based on the leave-subject-out
cross-validation. This method is applicable not only to averaging longitudinal data models …

In this article, we focus on prediction of a target model by transferring the information of
source models. To be flexible, we use semiparametric additive frameworks for the target and …

This article considers the problem of inference for nested least squares averaging
estimators. We study the asymptotic behavior of the Mallows model averaging estimator …