Scale matrix estimation of an elliptically symmetric distribution in high and low dimensions

Autor: Dominique Fourdrinier, Anis M. Haddouche, Fatiha Mezoued
Přispěvatelé: Ecole Nationale Supérieure de Statistique et d'Economie Appliquée [Tipaza] (ENSSEA), Laboratoire d'Informatique, de Traitement de l'Information et des Systèmes (LITIS), Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Institut national des sciences appliquées Rouen Normandie (INSA Rouen Normandie), Institut National des Sciences Appliquées (INSA)-Normandie Université (NU)-Institut National des Sciences Appliquées (INSA)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Journal of Multivariate Analysis
Journal of Multivariate Analysis, Elsevier, 2021, 181, pp.104680-. ⟨10.1016/j.jmva.2020.104680⟩
ISSN: 0047-259X
1095-7243
Popis: The problem of estimating the scale matrix Σ in a multivariate additive model, with elliptical noise, is considered from a decision-theoretic point of view. As the natural estimators of the form Σ ˆ a = a S (where S is the sample covariance matrix and a is a positive constant) perform poorly, we propose estimators of the general form Σ ˆ a , G = a ( S + S S + G ( Z , S ) ) , where S + is the Moore–Penrose inverse of S and G ( Z , S ) is a correction matrix. We provide conditions on G ( Z , S ) such that Σ ˆ a , G improves over Σ ˆ a under the quadratic loss L ( Σ , Σ ˆ ) = tr ( Σ ˆ Σ − 1 − I p ) 2 . We adopt a unified approach to the two cases where S is invertible and S is singular. To this end, a new Stein–Haff type identity and calculus on eigenstructure for S are developed. Our theory is illustrated with a large class of estimators which are orthogonally invariant.
Databáze: OpenAIRE