Arbeitspapier
Robust adaptive estimation of dimension reduction space
Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy tailed distributions. We show that the recently proposed MAVE and OPG methods by Xia et al. (2002) allow us to make them robust in a relatively straightforward way that preserves all advantages of the original approach. The best of the proposed robust modifications, which we refer to as MAVE-WMAD-R, is sufficiently robust to outliers and data from heavy tailed distributions, it is easy to implement, and surprisingly, it also outperforms the original method in small sample behaviour even when applied to normally distributed data.
- Sprache
-
Englisch
- Erschienen in
-
Series: SFB 373 Discussion Paper ; No. 2003,1
- Klassifikation
-
Wirtschaft
- Thema
-
nonparametric regression
dimension reduction
minimum average variance estimator
robust estimation
median absolute deviation
L1 regression
Nichtparametrisches Verfahren
Robustes Verfahren
Schätztheorie
Theorie
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Čížek, Pavel
Härdle, Wolfgang
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
- (wo)
-
Berlin
- (wann)
-
2003
- Handle
- URN
-
urn:nbn:de:kobv:11-10049716
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Čížek, Pavel
- Härdle, Wolfgang
- Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
Entstanden
- 2003
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