A General Framework For Frequentist Model Averaging

TL;DR

This paper introduces a comprehensive frequentist model averaging framework that combines estimators from multiple models, improving inference accuracy by accounting for model uncertainty and optimizing weights to minimize mean squared error.

Contribution

It develops a general, unrestricted framework for frequentist model averaging, deriving optimal weights and demonstrating improved performance over existing methods.

Findings

Proposed a flexible framework applicable to any set of candidate models.

Derived optimal weights that minimize expected mean squared error.

Simulation results show superior performance compared to traditional methods.

Abstract

Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. This practice does not account for the uncertainty introduced by the selection process and the fact that the selected model can possibly be a wrong one. Model averaging approaches try to remedy this issue by combining estimators for a set of candidate models. Specifically, instead of deciding which model is the 'right' one, a model averaging approach suggests to fit a set of candidate models and average over the estimators using certain data adaptive weights. In this paper we establish a general frequentist model averaging framework that does not set any restrictions on the set of candidate models. It greatly broadens the scope of the existing methodologies under the frequentist model averaging development. Assuming the data is from an unknown model, we derive the model averaging estimator and study its limiting distributions and related predictions while taking possible modeling biases into account. We propose a set of optimal weights to combine the individual estimators so that the expected mean squared error of the average estimator is minimized. Simulation studies are conducted to compare the performance of the estimator with that of the existing methods. The results show the benefits of the proposed approach over traditional model selection approaches as well as existing model averaging methods.