Arbeitspapier
Smoothing parameter selection in two frameworks for penalized splines
There are two popular smoothing parameter selection methods for spline smoothing. First, criteria that approximate the average mean squared error of the estimator (e.g. generalized cross validation) are widely used. Alternatively, the maximum likelihood paradigm can be employed under the assumption that the underlying function to be estimated is a realization of some stochastic process. In this article the asymptotic properties of both smoothing parameter estimators are studied and compared in the frequentist and stochastic framework for penalized spline smoothing. Consistency and asymptotic normality of the estimators are proved and small sample properties are discussed. A simulation study and a real data example illustrate the theoretical fi ndings.
- Language
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Englisch
- Bibliographic citation
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Series: Discussion Papers ; No. 85
- Classification
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Wirtschaft
- Subject
-
Maximum likelihood
Mean squared error minimizer
Penalized splines
Smoothing splines
- Event
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Geistige Schöpfung
- (who)
-
Krivobokova, Tatyana
- Event
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Veröffentlichung
- (who)
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Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
- (where)
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Göttingen
- (when)
-
2011
- Handle
- Last update
-
10.03.2025, 11:45 AM CET
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
- Krivobokova, Tatyana
- Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
Time of origin
- 2011
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