Strong Approximations for Empirical Processes Indexed by Lipschitz Functions

This paper presents new uniform Gaussian strong approximations for empirical processes indexed by classes of functions based on $d$-variate random vectors ($d\geq1$). First, a uniform Gaussian strong approximation is established for general empirical processes indexed by Lipschitz functions, encompassing and improving on all previous results in the literature. When specialized to the setting considered by Rio (1994), and certain constraints on the function class hold, our result improves the approximation rate $n^{-1/(2d)}$ to $n^{{-1/\max{d,2}}$,} up to the same $\operatorname{polylog} n$ term, where $n$ denotes the sample size. Remarkably, we establish a valid uniform Gaussian strong approximation at the optimal rate $n^{-1/2}\log n$ for $d=2$, which was previously known to be valid only for univariate ($d=1$) empirical processes via the celebrated Hungarian construction (Koml\'os et al., 1975). Second, a uniform Gaussian strong approximation is established for a class of multiplicative separable empirical processes indexed by Lipschitz functions, which address some outstanding problems in the literature (Chernozhukov et al., 2014, Section 3). In addition, two other uniform Gaussian strong approximation results are presented for settings where the function class takes the form of a sequence of Haar basis based on generalized quasi-uniform partitions. We demonstrate the improvements and usefulness of our new strong approximation results with several statistical applications to nonparametric density and regression estimation.