Refined Cram\'er Type Moderate Deviation Theorems for General Self-normalized Sums with Applications to Dependent Random Variables and Winsorized Mean

TL;DR

This paper develops a refined Cramér type moderate deviation theorem for self-normalized sums, extending classical results and applying them to dependent variables and winsorized means for improved probabilistic bounds.

Contribution

It introduces a unified, refined theorem for self-normalized sums that extends previous results and applies to dependent data and winsorized means.

Findings

Improved moderate deviation bounds for one-dependent variables

Enhanced results for geometrically eta-mixing variables

New theorems for self-normalized winsorized mean

Abstract

Let {(X_i,Y_i)}_{i=1}^n be a sequence of independent bivariate random vectors. In this paper, we establish a refined Cram\'er type moderate deviation theorem for the general self-normalized sum \sum_{i=1}^n X_i/(\sum_{i=1}^n Y_i^2)^{1/2}, which unifies and extends the classical Cram\'er (1938) theorem and the self-normalized Cram\'er type moderate deviation theorems by Jing, Shao and Wang (2003) as well as the further refined version by Wang (2011). The advantage of our result is evidenced through successful applications to weakly dependent random variables and self-normalized winsorized mean. Specifically, by applying our new framework on general self-normalized sum, we significantly improve Cram\'er type moderate deviation theorems for one-dependent random variables, geometrically \beta-mixing random variables and causal processes under geometrical moment contraction. As an additional application, we also derive the Cram\'er type moderate deviation theorems for self-normalized winsorized mean.