Model Averaging Estimation for Partially Linear Functional Score Models

TL;DR

This paper develops a Mallows-type criterion for model averaging in partially linear functional score models, achieving asymptotic optimality and demonstrating superior performance over existing methods through simulations and real data applications.

Contribution

It introduces a novel model averaging estimator with a Mallows-type criterion for partially linear functional score models, addressing unobservable nonparametric components.

Findings

Estimator is asymptotically optimal under regularity conditions

Simulation studies show superiority or comparability to existing methods

Applied successfully to real data sets

Abstract

This paper is concerned with model averaging estimation for partially linear functional score models. These models predict a scalar response using both parametric effect of scalar predictors and non-parametric effect of a functional predictor. Within this context, we develop a Mallows-type criterion for choosing weights. The resulting model averaging estimator is proved to be asymptotically optimal under certain regularity conditions in terms of achieving the smallest possible squared error loss. Simulation studies demonstrate its superiority or comparability to information criterion score-based model selection and averaging estimators. The proposed procedure is also applied to two real data sets for illustration. That the components of nonparametric part are unobservable leads to a more complicated situation than ordinary partially linear models (PLM) and a different theoretical derivation from those of PLM.