Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/44947 
Erscheinungsjahr: 
2009
Schriftenreihe/Nr.: 
Discussion Papers in Statistics and Econometrics No. 3/07
Verlag: 
University of Cologne, Seminar of Economic and Social Statistics, Cologne
Zusammenfassung: 
Many different robust estimation approaches for the covariance or shape matrix of multivariate data have been established until today. Tyler's M-estimator has been recognized as the 'most robust' M-estimator for the shape matrix of elliptically symmetric distributed data. Tyler's Mestimators 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.
Schlagwörter: 
covariance matrix
distribution-free estimation
missing data
robust estimation
shape matrix
sign-based estimator
Tyler's M-estimator
JEL: 
H12
H20
Dokumentart: 
Working Paper

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