Robust Semiparametric Efficient Estimators in Complex Elliptically Symmetric Distributions

Autor: Alexandre Renaux, Frédéric Pascal, Stefano Fortunati

Publikováno v: IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2020, 68, pp.5003-5015. ⟨10.1109/TSP.2020.3019110⟩

International audience; Covariance matrices play a major role in statistics , signal processing and machine learning applications. This paper focuses on the semiparametric covariance/scatter matrix estimation problem in elliptical distributions. The

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48cf9e7d07aff6079c8dbad8be481391
https://doi.org/10.1109/tsp.2020.3019110

Robust Estimation of Structured Scatter Matrices in (Mis)matched Models

Autor: Bruno Meriaux, Philippe Forster, Arnaud Breloy, Mohammed Nabil El Korso, Chengfang Ren

Publikováno v: Signal Processing
Signal Processing, 2019, 165, pp.163-174. ⟨10.1016/j.sigpro.2019.06.030⟩
Signal Processing, Elsevier, 2019, 165, pp.163-174. ⟨10.1016/j.sigpro.2019.06.030⟩

International audience; Covariance matrix estimation is a ubiquitous problem in signal processing. In most modern signal processing applications, data are generally modeled by non-Gaussian distributions with covariance matrices exhibiting a particula

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ced6344432c8477a28377018518a7083
https://hal.science/hal-02165848

Semiparametric Inference and Lower Bounds for Real Elliptically Symmetric Distributions

Autor: Fulvio Gini, Muralidhar Rangaswamy, Maria Greco, Abdelhak M. Zoubir, Stefano Fortunati

This paper has a twofold goal. The first aim is to provide a deeper understanding of the family of the Real Elliptically Symmetric (RES) distributions by investigating their intrinsic semiparametric nature. The second aim is to derive a semiparametri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a07e534dd55ea204d4048a3bf31eea1

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