Multiplier U-processes: sharp bounds and applications

TL;DR

This paper develops a theoretical framework for multiplier U-processes, extending empirical process theory to higher-order cases, with applications in bootstrap methods, M-estimators, and complex sampling designs.

Contribution

It introduces a new multiplier inequality for U-processes, enabling advanced analysis and applications in statistical inference involving U-statistics.

Findings

Established a multiplier inequality for U-processes.

Derived bootstrap CLTs for U-processes.

Provided theoretical foundations for M-estimation with U-statistics.

Abstract

The theory for multiplier empirical processes has been one of the central topics in the development of the classical theory of empirical processes, due to its wide applicability to various statistical problems. In this paper, we develop theory and tools for studying multiplier $U$-processes, a natural higher-order generalization of the multiplier empirical processes. To this end, we develop a multiplier inequality that quantifies the moduli of continuity of the multiplier $U$-process in terms of that of the (decoupled) symmetrized $U$-process. The new inequality finds a variety of applications including (i) multiplier and bootstrap central limit theorems for $U$-processes, (ii) general theory for bootstrap $M$-estimators based on $U$-statistics, and (iii) theory for $M$-estimation under general complex sampling designs, again based on $U$-statistics.