Table of Contents
Fetching ...

On the maximal correlation of some stochastic processes

Yinshan Chang, Qinwei Chen

Abstract

We study the maximal correlation coefficient $R(X,Y)$ between two stochastic processes $X$ and $Y$. In the case when $(X,Y)$ is a random walk, we find $R(X,Y)$ using the Csáki-Fischer identity and the lower semicontinuity of the map $\text{Law}(X,Y) \to R(X,Y)$. When $(X,Y)$ is a two-dimensional Lévy process, we express $R(X,Y)$ in terms of the Lévy measure of the process and the covariance matrix of the diffusion part of the process. Consequently, for a two-dimensional $α$-stable random vector $(X,Y)$ with $0<α<2$, we express $R(X,Y)$ in terms of $α$ and the spectral measure $τ$ of the $α$-stable distribution. We also establish analogs and extensions of the Dembo-Kagan-Shepp-Yu inequality and the Madiman-Barron inequality.

On the maximal correlation of some stochastic processes

Abstract

We study the maximal correlation coefficient between two stochastic processes and . In the case when is a random walk, we find using the Csáki-Fischer identity and the lower semicontinuity of the map . When is a two-dimensional Lévy process, we express in terms of the Lévy measure of the process and the covariance matrix of the diffusion part of the process. Consequently, for a two-dimensional -stable random vector with , we express in terms of and the spectral measure of the -stable distribution. We also establish analogs and extensions of the Dembo-Kagan-Shepp-Yu inequality and the Madiman-Barron inequality.

Paper Structure

This paper contains 27 sections, 22 theorems, 254 equations, 2 tables.

Key Result

Theorem 1.1

For any $m\ge 1$, $\blacktriangleleft$$\blacktriangleleft$

Theorems & Definitions (45)

  • Theorem 1.1
  • Theorem 1.2
  • Theorem 1.3
  • Theorem 1.4: Madiman-Barron
  • Remark 1.1
  • Remark 1.2
  • Theorem 1.5
  • Theorem 1.6
  • Corollary 1.7: Dembo-Kagan-Shepp-Yu
  • Remark 1.3
  • ...and 35 more