Matrix moments in a real, doubly correlated algebraic generalization of the Wishart model

The Wishart model of random covariance or correlation matrices continues to find ever more applications as the wealth of data on complex systems of all types grows. The heavy tails often encountered prompt generalizations of the Wishart model, involving algebraic distributions instead of a Gaussian. The mathematical properties pose new challenges, particularly for the doubly correlated versions. Here we investigate such a doubly correlated algebraic model for real covariance or correlation matrices, which is known as matrix variate t-distribution in the statistics literature. We focus on the matrix moments and explicitly calculate the first and the second one, the computation of the latter is non-trivial. We solve the problem by relating it to the Aomoto integral and by extending the recursive technique to calculate Ingham-Siegel integrals. We compare our results with the Gaussian case.

Automated summary

The study focuses on a doubly correlated algebraic model for real covariance or correlation matrices, known as the matrix variate t-distribution. It explores matrix moments, specifically calculating the first and second moments, with the computation of the latter being non-trivial. The problem is solved by relating it to the Aomoto integral and extending the recursive technique to calculate Ingham-Siegel integrals, with results compared to the Gaussian case.