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Gabriel Frahm, University ok Koeln Print
Thursday, 21 October 2010, 12:15 - 13:15

A Generalization of Tyler's M-Estimators to the Case of Incomplete Data

Abstract:  Many different robust estimation approaches for the covariance or shape matrix of multivariate data have been established. Tyler's M-estimator has been recognized as the ''most robust'' M-estimator for the shape matrix of elliptically symmetric distributed data. Tyler's M-estimators for location and shape are generalized by taking account of incomplete data. It is shown that the shape matrix estimator remains distribution-free under the class of generalized elliptical distributions. Its asymptotic distribution is also derived and a fast algorithm, which works well even for high-dimensional data, is presented. A simulation study with clean and contaminated data covers the complete-data as well as the incomplete-data case, where the missing data are assumed to be MCAR, MAR, and NMAR.

Gabriel Frahm, University of Koeln

Location: S 12.227
Contact: Claude Adan, This e-mail address is being protected from spam bots, you need JavaScript enabled to view it