Efron's monotonicity property for measures on $\mathbb{R}^2$
Statistics Theory
2017-12-22 v2 Probability
Statistics Theory
Abstract
First we prove some kernel representations for the covariance of two functions taken on the same random variable and deduce kernel representations for some functionals of a continuous one-dimensional measure. Then we apply these formulas to extend Efron's monotonicity property, given in Efron [1965] and valid for independent log-concave measures, to the case of general measures on . The new formulas are also used to derive some further quantitative estimates in Efron's monotonicity property.
Cite
@article{arxiv.1707.04472,
title = {Efron's monotonicity property for measures on $\mathbb{R}^2$},
author = {Adrien Saumard and Jon A. Wellner},
journal= {arXiv preprint arXiv:1707.04472},
year = {2017}
}