Arbeitspapier
Asymptotic distributions of robust shape matrices and scales
It has been frequently observed in the literature that many multivariate statistical methods require the covariance or dispersion matrix ∑ of an elliptical distribution only up to some scaling constant. If the topic of interest is not the scale but only the shape of the elliptical distribution, it is not meaningful to focus on the asymptotic distribution of an estimator for ∑ or another matrix Γ ∝ ∑. In the present work, robust estimators for the shape matrix and the associated scale are investigated. Explicit expressions for their joint asymptotic distributions are derived. It turns out that if the joint asymptotic distribution is normal, the presented estimators are asymptotically independent for one and only one specific choice of the scale function. If it is non-normal (this holds for example if the estimators for the shape matrix and scale are based on the minimum volume ellipsoid estimator) only the presented scale function leads to asymptotically uncorrelated estimators. This is a generalization of a result obtained by Paindaveine (2008) in the context of local asymptotic normality theory.
- Sprache
-
Englisch
- Erschienen in
-
Series: Discussion Papers in Statistics and Econometrics ; No. 5/07
- Klassifikation
-
Wirtschaft
Taxation, Subsidies, and Revenue: General
Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy: General (includes Measurement and Data)
- Thema
-
local asymptotic normality
M-estimator
R-estimator
robust covariance matrix estimator
scale-invariant function
S-estimator
shape matrix
Tyler's M-estimator
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Frahm, Gabriel
- Ereignis
-
Veröffentlichung
- (wer)
-
University of Cologne, Seminar of Economic and Social Statistics
- (wo)
-
Cologne
- (wann)
-
2008
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 MEZ
Datenpartner
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Objekttyp
- Arbeitspapier
Beteiligte
- Frahm, Gabriel
- University of Cologne, Seminar of Economic and Social Statistics
Entstanden
- 2008
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