  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.
