Risk and resampling under model uncertainty

TL;DR

This paper explores the use of Bayesian model averaging to improve estimation under model uncertainty, establishing risk bounds and bootstrap consistency, and proposing a data-adaptive scheme for better efficiency and applicability.

Contribution

It introduces a new data-adaptive model averaging method that ensures risk bounds and bootstrap validity under fixed parameters, enhancing estimation reliability.

Findings

Bayesian model-averaged estimator risk is bounded with fixed parameters.

Bootstrap can consistently approximate the distribution of the model-averaged estimator.

Proposed scheme balances estimation efficiency and bootstrap applicability.

Abstract

In statistical exercises where there are several candidate models, the traditional approach is to select one model using some data driven criterion and use that model for estimation, testing and other purposes, ignoring the variability of the model selection process. We discuss some problems associated with this approach. An alternative scheme is to use a model-averaged estimator, that is, a weighted average of estimators obtained under different models, as an estimator of a parameter. We show that the risk associated with a Bayesian model-averaged estimator is bounded as a function of the sample size, when parameter values are fixed. We establish conditions which ensure that a model-averaged estimator's distribution can be consistently approximated using the bootstrap. A new, data-adaptive, model averaging scheme is proposed that balances efficiency of estimation without compromising applicability of the bootstrap. This paper illustrates that certain desirable risk and resampling properties of model-averaged estimators are obtainable when parameters are fixed but unknown; this complements several studies on minimaxity and other properties of post-model-selected and model-averaged estimators, where parameters are allowed to vary.