Smoothing parameter selection in two frameworks for penalized splines

Arbeitspapier

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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Discussion Papers ; No. 85

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Maximum likelihood ; Mean squared error minimizer ; Penalized splines ; Smoothing splines

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Krivobokova, Tatyana
• Ereignis: Veröffentlichung
• (wer): Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG)
• (wo): Göttingen
• (wann): 2011

Weitere Informationen

• Handle: http://hdl.handle.net/10419/90457
• Letzte Aktualisierung: 20.09.2024, 08:22 MESZ