Rectangular and Elliptical Probability Inequalities for Schur-Concave Random Variables

Abstract

It is shown that if the density $f(\mathbf{x})$ of $\mathbf{X} = (X_1, \cdots, X_n)$ is Schur-concave, then (1) $P(|X_i| \leq a_i, i = 1, \cdots, n)$ is a Schur-concave function of $(\phi(a_1), \cdots, \phi(a_n))$, and (2) $P\{\Sigma(X_i/a_i)^2 \leq 1\}$ is a Schur-concave function of $(\phi(a^2_1), \cdots, \phi(a^2_n))$, where $\phi; \lbrack 0, \infty) \rightarrow \lbrack 0, \infty)$ is any increasing and convex function. By letting $\phi(a) = a$, (1) implies that $P(|X_i| \leq a_i, i = 1, \cdots, n) \leq P(|X_i| \leq \bar{a}, i = 1, \cdots, n)$. As special consequences, the results yield bounds for exchangeable normal and $t$ variables and for linear combinations of central and noncentral Chi squared variables.