  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.
