On positivity of orthogonal series and its applications in probability

TL;DR

This paper establishes necessary and sufficient conditions for orthogonal series to converge to nonnegative functions, with applications in analysis and probability, including characterizations of Lancaster-type expansions and associated distribution classes.

Contribution

It provides new criteria for the positivity of orthogonal series and characterizes the distribution classes admitting Lancaster-type expansions.

Findings

Conditions for orthogonal series to converge to nonnegative functions.

Characterization of Lancaster-type expansions and their convergence.

Identification of distribution classes with polynomial conditional moments.

Abstract

We give necessary and sufficient conditions for an orthogonal series to converge in the mean-squares to a nonnegative function. We present many examples and applications, in analysis and probability. In particular, we give necessary and sufficient conditions for a Lancaster-type of expansion $% \sum_{n\geq 0}c_{n}\alpha _{n}(x)\beta _{n}(y)$ with two sets of orthogonal polynomials $\left\{ \alpha _{n}\right\} $ and $\left\{ \beta _{n}\right\} $ to converge in means-squares to a nonnegative bivariate function. In particular, we study the properties of the set $C(\alpha ,\beta )$ of the sequences $\left\{ c_{n}\right\} ,$ for which the above-mentioned series converge to a nonnegative function and give conditions for the membership to it. Further we show that the class of bivariate distributions for which a Lancaster type expansion can be found, is the same as the class of distributions having all conditional moments in the form of polynomials in the conditioning random variable.