Robust semiparametric joint estimators of location and scatter in elliptical distributions

Stefano Fortunati; Alexandre Renaux; Frederic Pascal

doi:10.1109/MLSP49062.2020.9231865

Robust semiparametric joint estimators of location and scatter in elliptical distributions

Stefano Fortunati, Alexandre Renaux, Frederic Pascal

Université Paris-Saclay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper focuses on the joint estimation of the location vector and the shape matrix of a set of Complex Elliptically Symmetric (CES) distributed observations. This well-known estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an infinite-dimensional nuisance parameter. A joint estimator, relying on the Tyler's M-estimator of location and on a new R-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cramer-Rao Bound (CSCRB).

Original language	English
Title of host publication	Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728166629
DOIs	https://doi.org/10.1109/MLSP49062.2020.9231865
Publication status	Published - 1 Sept 2020
Externally published	Yes
Event	30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020 - Virtual, Espoo, Finland Duration: 21 Sept 2020 → 24 Sept 2020

Publication series

Name	IEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume	2020-September
ISSN (Print)	2161-0363
ISSN (Electronic)	2161-0371

Conference

Conference	30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020
Country/Territory	Finland
City	Virtual, Espoo
Period	21/09/20 → 24/09/20

Keywords

Covariance estimation
Efficiency
Elliptical distribution
Robust estimator
Semiparametric model

Access to Document

10.1109/MLSP49062.2020.9231865

Cite this

Fortunati, S., Renaux, A., & Pascal, F. (2020). Robust semiparametric joint estimators of location and scatter in elliptical distributions. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020 Article 9231865 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2020-September). IEEE Computer Society. https://doi.org/10.1109/MLSP49062.2020.9231865

Fortunati, Stefano ; Renaux, Alexandre ; Pascal, Frederic. / Robust semiparametric joint estimators of location and scatter in elliptical distributions. Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE Computer Society, 2020. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP).

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title = "Robust semiparametric joint estimators of location and scatter in elliptical distributions",

abstract = "This paper focuses on the joint estimation of the location vector and the shape matrix of a set of Complex Elliptically Symmetric (CES) distributed observations. This well-known estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an infinite-dimensional nuisance parameter. A joint estimator, relying on the Tyler's M-estimator of location and on a new R-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cramer-Rao Bound (CSCRB).",

keywords = "Covariance estimation, Efficiency, Elliptical distribution, Robust estimator, Semiparametric model",

author = "Stefano Fortunati and Alexandre Renaux and Frederic Pascal",

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year = "2020",

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Fortunati, S, Renaux, A & Pascal, F 2020, Robust semiparametric joint estimators of location and scatter in elliptical distributions. in Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020., 9231865, IEEE International Workshop on Machine Learning for Signal Processing, MLSP, vol. 2020-September, IEEE Computer Society, 30th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2020, Virtual, Espoo, Finland, 21/09/20. https://doi.org/10.1109/MLSP49062.2020.9231865

Robust semiparametric joint estimators of location and scatter in elliptical distributions. / Fortunati, Stefano; Renaux, Alexandre; Pascal, Frederic.
Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE Computer Society, 2020. 9231865 (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2020-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Renaux, Alexandre

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N2 - This paper focuses on the joint estimation of the location vector and the shape matrix of a set of Complex Elliptically Symmetric (CES) distributed observations. This well-known estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an infinite-dimensional nuisance parameter. A joint estimator, relying on the Tyler's M-estimator of location and on a new R-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cramer-Rao Bound (CSCRB).

AB - This paper focuses on the joint estimation of the location vector and the shape matrix of a set of Complex Elliptically Symmetric (CES) distributed observations. This well-known estimation problem is framed in the original context of semiparametric models allowing us to handle the (generally unknown) density generator as an infinite-dimensional nuisance parameter. A joint estimator, relying on the Tyler's M-estimator of location and on a new R-estimator of shape matrix, is proposed and its Mean Squared Error (MSE) performance compared with the Semiparametric Cramer-Rao Bound (CSCRB).

KW - Covariance estimation

KW - Efficiency

KW - Elliptical distribution

KW - Robust estimator

KW - Semiparametric model

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T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP

BT - Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020

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Fortunati S, Renaux A, Pascal F. Robust semiparametric joint estimators of location and scatter in elliptical distributions. In Proceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020. IEEE Computer Society. 2020. 9231865. (IEEE International Workshop on Machine Learning for Signal Processing, MLSP). doi: 10.1109/MLSP49062.2020.9231865

Robust semiparametric joint estimators of location and scatter in elliptical distributions

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