Identifiability of parametric random matrix models

Published 27 Dec 2018 in math.PR, math.OA, math.ST, and stat.TH | (1812.10678v1)

Abstract: We investigate parameter identifiability of spectral distributions of random matrices. In particular, we treat compound Wishart type and signal-plus-noise type. We show that each model is identifiable up to some kind of rotation of parameter space. Our method is based on free probability theory.

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Authors (1)

Tomohiro Hayase

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