Corpus ID: 219176560

# Scale matrix estimation under data-based loss in high and low dimensions

@article{Haddouche2020ScaleME,
title={Scale matrix estimation under data-based loss in high and low dimensions},
author={Mohamed Anis Haddouche and Dominique Fourdrinier and Fatiha Mezoued},
journal={arXiv: Statistics Theory},
year={2020}
}
• Published 30 May 2020
• Mathematics
• arXiv: Statistics Theory
We consider the problem of estimating the scale matrix $\Sigma$ of the additif model $Y_{p\times n} = M + \mathcal{E}$, under a theoretical decision point of view. Here, $p$ is the number of variables, $n$ is the number of observations, $M$ is a matrix of unknown parameters with rank $q m$ (S non-invertible), we propose estimators of the form ${\hat{\Sigma}}_{a, G} = a\big( S+ S \, {S^{+}\,G(Z,S)}\big)$ where ${S^{+}}$ is the Moore-Penrose inverse of $S$ (which coincides with \$S^{-1… Expand

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