  In her recent paper [Negative dependence, scrambled nets, and variance
bounds. Math. Oper. Res. 43 (2018), 228-251] Christiane Lemieux studied a
framework to analyze the dependence structure of sampling schemes. The main
goal of the framework is to determine conditions under which the negative
dependence structure of a sampling scheme yields estimators with reduced
variance compared to Monte Carlo estimators. For instance, she was able to show
that in dimension d = 2 scrambled (0,m, d)-nets lead to randomized quasi-Monte
Carlo estimators with variance no larger than the variance of Monte Carlo
estimators for functions monotone in each variable. Her result relies on a
pairwise negative dependence property that is, in particular, satisfied by
(0,m, 2)-nets. In this note we establish that the same result holds true in
arbitrary dimension d for a type of randomized lattice point sets that we call
randomly shifted and jittered rank-1 lattices. We show that the details of the
randomization are crucial and that already small modifications may destroy the
pairwise negative dependence property.
