Le Cam spacings theorem in dimension two
统计理论
2007-06-13 v1 统计理论
摘要
The definition of spacings associated to a sequence of random variables is extended to the case of random vectors in [0,1]^2. Beirlant & al. (1991) give an alternative proof of the Le Cam (1958) theorem concerning asymptotic normality of additive functions of uniform spacings in [0,1]. I adapt their technique to the two-dimensional case, leading the way to new directions in the domain of Complete Spatial Randomness (CSR) testing.
引用
@article{arxiv.math/0507367,
title = {Le Cam spacings theorem in dimension two},
author = {Lionel Cucala},
journal= {arXiv preprint arXiv:math/0507367},
year = {2007}
}