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Maximum likelihood estimation for tensor normal models via castling transforms

Published online by Cambridge University Press:  01 July 2022

Harm Derksen
Affiliation:
Department of Mathematics, Northeastern University, 567 Lake Hall, 360 Huntington Ave, Boston, MA 02115, USA; E-mail: ha.derksen@northeastern.edu.
Visu Makam
Affiliation:
Radix Trading Europe B. V., Strawinskylaan 1217, Amsterdam, 1082 MK, Netherlands; E-mail: visu@umich.edu.
Michael Walter
Affiliation:
Faculty of Computer Science, Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum, Germany; E-mail: michael.walter@rub.de. Korteweg-de Vries Institute for Mathematics, Institute for Theoretical Physics, Institute for Logic, Language and Computation, QuSoft, University of Amsterdam, Science Park 105-107, 1098 XG Amsterdam, The Netherlands.

Abstract

In this paper, we study sample size thresholds for maximum likelihood estimation for tensor normal models. Given the model parameters and the number of samples, we determine whether, almost surely, (1) the likelihood function is bounded from above, (2) maximum likelihood estimates (MLEs) exist, and (3) MLEs exist uniquely. We obtain a complete answer for both real and complex models. One consequence of our results is that almost sure boundedness of the log-likelihood function guarantees almost sure existence of an MLE. Our techniques are based on invariant theory and castling transforms.

Information

Type
Computational Mathematics
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press