Orthogonal polynomial kernels and canonical correlations for Dirichlet measures
Robert C. Griffiths, Dario Span\`o

TL;DR
This paper characterizes orthogonal polynomial kernels and explores their role in understanding canonical correlations for Dirichlet measures and their limits, providing new identities and interpretations in multivariate settings.
Contribution
It offers a complete characterization of n-orthogonal polynomial kernels for Dirichlet distributions and extends classical results to multivariate and infinite-dimensional cases.
Findings
Derived identities and integral representations for polynomial kernels
Established probabilistic interpretations via dependent Polya urns
Extended solutions to Lancaster's problem to high-dimensional and limit distributions
Abstract
We consider a multivariate version of the so-called Lancaster problem of characterizing canonical correlation coefficients of symmetric bivariate distributions with identical marginals and orthogonal polynomial expansions. The marginal distributions examined in this paper are the Dirichlet and the Dirichlet multinomial distribution, respectively, on the continuous and the N-discrete d-dimensional simplex. Their infinite-dimensional limit distributions, respectively, the Poisson-Dirichlet distribution and Ewens's sampling formula, are considered as well. We study, in particular, the possibility of mapping canonical correlations on the d-dimensional continuous simplex (i) to canonical correlation sequences on the d+1-dimensional simplex and/or (ii) to canonical correlations on the discrete simplex, and vice versa. Driven by this motivation, the first half of the paper is devoted to…
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