Arbeitspapier
On the estimation of a monotone conditional variance in nonparametric regression
A monotone estimate of the conditional variance function in a heteroscedastic, nonpara- metric regression model is proposed. The method is based on the application of a kernel density estimate to an unconstrained estimate of the variance function and yields an esti- mate of the inverse variance function. The final monotone estimate of the variance function is obtained by an inversion of this function. The method is applicable to a broad class of nonparametric estimates of the conditional variance and particularly attractive to users of conventional kernel methods, because it does not require constrained optimization techniques. The approach is also illustrated by means of a simulation study
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2004,42
- Subject
-
nonparametric regression
heteroscedasticity
variance function
monotonicity
order restricted inference
Regression
Nichtparametrisches Verfahren
Schätztheorie
Theorie
- Event
-
Geistige Schöpfung
- (who)
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Dette, Holger
Pilz, Kay F.
- Event
-
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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2004
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
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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
- Dette, Holger
- Pilz, Kay F.
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2004
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