Smoothing parameter selection in two frameworks for penalized splines

Tatyana Krivobokova
Additional contact information
Tatyana Krivobokova: Georg-August-University Göttingen

No 85, Courant Research Centre: Poverty, Equity and Growth - Discussion Papers from Courant Research Centre PEG

Abstract: 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.

Keywords: Maximum likelihood; Mean squared error minimizer; Penalized splines; Smoothing splines (search for similar items in EconPapers)
Date: 2011-08-02, Revised 2012-10-18
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www2.vwl.wiso.uni-goettingen.de/courant-papers/CRC-PEG_DP_85.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:got:gotcrc:085

Access Statistics for this paper

More papers in Courant Research Centre: Poverty, Equity and Growth - Discussion Papers from Courant Research Centre PEG Platz der Goettinger Sieben 3; D-37073 Goettingen, GERMANY.
Bibliographic data for series maintained by Dominik Noe (dnoe@uni-goettingen.de).