Loss Functions in Restricted Parameter Spaces and Their Bayesian Applications

TL;DR

This paper introduces new loss functions tailored for Bayesian estimation in restricted parameter spaces, addressing limitations of squared error loss and demonstrating their advantages through four Bayesian problems.

Contribution

It proposes generalized loss functions for parameters on positive real lines and intervals, with explicit Bayes estimators and multivariate extensions, improving inference in constrained settings.

Findings

New loss functions penalize boundary decisions more effectively.

Explicit Bayes estimators are derived for restricted parameter spaces.

Demonstrated benefits in four Bayesian estimation problems.

Abstract

Squared error loss remains the most commonly used loss function for constructing a Bayes estimator of the parameter of interest. However, it can lead to sub-optimal solutions when a parameter is defined in a restricted space. It can also be an inappropriate choice in the context when an extreme overestimation and/or underestimation results in severe consequences and a more conservative estimator is preferred. We advocate a class of loss functions for parameters defined on restricted spaces which infinitely penalize boundary decisions like the squared error loss does on the real line. We also recall several properties of loss functions such as symmetry, convexity, and invariance. We propose generalizations of the squared error loss function for parameters defined on the positive real line and on an interval. We provide explicit solutions for corresponding Bayes estimators and discuss multivariate extensions. {Four} well-known Bayesian estimation problems are used to demonstrate inferential benefits the novel Bayes estimators can provide in the context of restricted estimation.