Saddlepoint approximations for likelihood ratio like statistics with applications to permutation tests

TL;DR

This paper develops saddlepoint approximation theorems for likelihood ratio-like statistics in multiparameter problems, enabling more accurate permutation and rank tests, with potential applications to bootstrap methods.

Contribution

It introduces two theorems extending saddlepoint approximations to cases without densities and provides multivariate generalizations of key formulas, improving permutation test accuracy.

Findings

High accuracy of saddlepoint approximations demonstrated in numerical examples

Improved permutation tests for k-sample and multivariate two-sample problems

Comparison shows advantages over classical statistics

Abstract

We obtain two theorems extending the use of a saddlepoint approximation to multiparameter problems for likelihood ratio-like statistics which allow their use in permutation and rank tests and could be used in bootstrap approximations. In the first, we show that in some cases when no density exists, the integral of the formal saddlepoint density over the set corresponding to large values of the likelihood ratio-like statistic approximates the true probability with relative error of order $1/n$. In the second, we give multivariate generalizations of the Lugannani--Rice and Barndorff-Nielsen or $r^*$ formulas for the approximations. These theorems are applied to obtain permutation tests based on the likelihood ratio-like statistics for the $k$ sample and the multivariate two-sample cases. Numerical examples are given to illustrate the high degree of accuracy, and these statistics are compared to the classical statistics in both cases.