Arbeitspapier
Nonparametric M-estimation with long-memory errors
We investigate the behavior of nonparametric kernel M-estimators in the presence of long-memory errors. The optimal bandwidth and a central limit theorem are obtained. It turns out that in the Gaussian case all kernel M-estimators have the same limiting normal distribution. The motivation behind this study is illustrated with an example.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2000,36
- Subject
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Zeitreihenanalyse
Nichtparametrisches Verfahren
Theorie
Statistischer Fehler
- Event
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Geistige Schöpfung
- (who)
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Beran, Jan
Ghosh, Sucharita
Sibbertsen, Philipp
- Event
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Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
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Dortmund
- (when)
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2000
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Beran, Jan
- Ghosh, Sucharita
- Sibbertsen, Philipp
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2000
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