Delta method in large deviations and moderate deviations for estimators

TL;DR

This paper introduces a general delta method within large and moderate deviation theory to improve tail probability approximations for estimators, extending its applications to various statistical estimators and hypothesis testing.

Contribution

A novel delta method in large deviations that broadens the scope of moderate deviation analysis for estimators and improves existing results.

Findings

Applied to Wilcoxon statistic, Kaplan--Meier estimator, and empirical processes.

Enhanced moderate deviation results for M-estimators and L-statistics.

Demonstrated applications in statistical hypothesis testing.

Abstract

The delta method is a popular and elementary tool for deriving limiting distributions of transformed statistics, while applications of asymptotic distributions do not allow one to obtain desirable accuracy of approximation for tail probabilities. The large and moderate deviation theory can achieve this goal. Motivated by the delta method in weak convergence, a general delta method in large deviations is proposed. The new method can be widely applied to driving the moderate deviations of estimators and is illustrated by examples including the Wilcoxon statistic, the Kaplan--Meier estimator, the empirical quantile processes and the empirical copula function. We also improve the existing moderate deviations results for $M$-estimators and $L$-statistics by the new method. Some applications of moderate deviations to statistical hypothesis testing are provided.