A practical procedure to find matching priors for frequentist inference

TL;DR

This paper introduces a practical method for identifying matching priors in frequentist inference, utilizing saddlepoint approximations to improve test performance in small samples.

Contribution

It develops a flexible procedure combining saddlepoint approximations with matching priors, allowing adjustment of test performance in small sample scenarios.

Findings

Coverage verified via Monte Carlo simulation

Effective in small sample settings

Applicable to exponential ratio and logistic regression models

Abstract

In the manuscript, we present a practical way to find the matching priors proposed by Welch & Peers (1963) and Peers (1965). We investigate the use of saddlepoint approximations combined with matching priors and obtain p-values of the test of an interest parameter in the presence of nuisance parameter. The advantage of our procedure is the flexibility of choosing different initial conditions so that one can adjust the performance of the test. Two examples have been studied, with coverage verified via Monte Carlo simulation. One relates to the ratio of two exponential means, and the other relates the logistic regression model. Particularly, we are interested in small sample settings.