Density estimation by Randomized Quasi-Monte Carlo

TL;DR

This paper explores how randomized quasi-Monte Carlo and stratified sampling can improve the accuracy of kernel density estimators by reducing variance and mean integrated square error, especially in high-dimensional problems.

Contribution

It demonstrates both theoretically and empirically that RQMC and stratification can significantly reduce estimation errors and achieve faster convergence than traditional Monte Carlo methods.

Findings

RQMC and stratified sampling reduce variance and MISE.

Faster convergence rates than MC in some cases.

Effective in high-dimensional settings.

Abstract

We consider the problem of estimating the density of a random variable $X$ that can be sampled exactly by Monte Carlo (MC). We investigate the effectiveness of replacing MC by randomized quasi Monte Carlo (RQMC) or by stratified sampling over the unit cube, to reduce the integrated variance (IV) and the mean integrated square error (MISE) for kernel density estimators. We show theoretically and empirically that the RQMC and stratified estimators can achieve substantial reductions of the IV and the MISE, and even faster convergence rates than MC in some situations, while leaving the bias unchanged. We also show that the variance bounds obtained via a traditional Koksma-Hlawka-type inequality for RQMC are much too loose to be useful when the dimension of the problem exceeds a few units. We describe an alternative way to estimate the IV, a good bandwidth, and the MISE, under RQMC or stratification, and we show empirically that in some situations, the MISE can be reduced significantly even in high-dimensional settings.