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On the Maximum Likelihood Estimator Statistics for Unimodal Elliptical Distributions in the High Signal-to-Noise Ratio Regime

Abstract : In this paper, we study the behavior of the maximum likelihood estimator in the framework of low noise level (or high signal-to-noise ratio), when the data follow an unimodal elliptical distribution. The maximum likelihood estimator appears to be the same as in the Gaussian context, regardless the noise distribution. We also show that the asymptotic distribution of this estimator is unimodal elliptical, where the law is intimately linked to that of the noise distribution. Additionally, this estimator is shown to be not efficient, except in the Gaussian noise case. Finally, we validate our analytic results by some simulations. Index Terms-Maximum likelihood estimator statistics, high signal-to-noise ratio regime, elliptically symmetric distribution.
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Submitted on : Wednesday, February 26, 2020 - 2:08:54 PM
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Steeve Zozor, Chengfang Ren, Alexandre Renaux. On the Maximum Likelihood Estimator Statistics for Unimodal Elliptical Distributions in the High Signal-to-Noise Ratio Regime. IEEE Signal Processing Letters, Institute of Electrical and Electronics Engineers, 2018, 25 (6), pp.883 - 887. ⟨10.1109/LSP.2018.2831178⟩. ⟨hal-01798888⟩

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