Maximum likelihood estimation for tensor normal models via castling transforms
نویسندگان
چکیده
Abstract In this paper, we study sample size thresholds for maximum likelihood estimation tensor normal models. Given the model parameters and number of samples, determine whether, almost surely, (1) function is bounded from above, (2) estimates (MLEs) exist, (3) MLEs exist uniquely. We obtain a complete answer both real complex One consequence our results that sure boundedness log-likelihood guarantees existence an MLE. Our techniques are based on invariant theory castling transforms.
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ژورنال
عنوان ژورنال: Forum of Mathematics, Sigma
سال: 2022
ISSN: ['2050-5094']
DOI: https://doi.org/10.1017/fms.2022.37