Some improved Gaussian correlation inequalities for symmetrical n-rectangles extended to some multivariate gamma distributions and some further probability inequalities

TL;DR

This paper improves Gaussian correlation inequalities for symmetrical n-rectangles by extending them to certain multivariate gamma distributions, introducing new probability inequalities and methods for correlation matrix adjustments.

Contribution

It introduces novel inequalities for multivariate gamma distributions and extends Gaussian correlation inequalities using MTP2 properties and correlation matrix modifications.

Findings

MTP2-cdfs include some multivariate chi-square distributions.

New probability inequalities for multivariate gamma distributions.

Enhanced Gaussian correlation inequalities with specific correlation structures.

Abstract

The Gaussian correlation inequality (GCI) for symmetrical n-rectangles is improved if the absolute components have a joint cumulative distribution (cdf) which is MTP2 (multivariate totally positive of order 2). Inequalities of the here given type hold at least for all MTP2-cdfs on R^n or (0,infinity)^n with everywhere positive smooth densities. In particular, at least some infinitely divisible multivariate chi-square distribution functions (gamma distributions in the sense of Krishnamoorthy and Parthasarathy) with any positive real "degree of freedom" are shown to be MTP2. Moreover, further numerically calculable probability inequalities for a broad class of multivariate gamma distributions are derived and a different improvement for inequalities of the GCI-type - and of a similar type with three instead of two groups of components - with more special correlation structures. The main idea behind these inequalities is to find for a given correlation matrix with positive correlations a further correlation matrix with smaller correlations whose inverse is an M-matrix and where the corresponding multivariate gamma distribution function is numerically available.