  In a very recent work, Basu and Owen (2015) propose the use of scrambled
geometric nets in numerical integration when the domain is a product of $s$
arbitrary spaces of dimension $d$ having a certain partitioning constraint. It
was shown that for a class of smooth functions, the integral estimate has
variance $O( n^{-1 -2/d} (\log n)^{s-1})$ for scrambled geometric nets,
compared to $O(n^{-1})$ for ordinary Monte Carlo.
  The main idea of this paper is to develop on the work by Loh (2003), to show
that the scrambled geometric net estimate has an asymptotic normal distribution
for certain smooth functions defined on products of suitable subsets of
$\mathbb{R}^d$.
