Smoothing parameter selection in two frameworks for penalized splines

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Abstract

There are two popular smoothing parameter selection methods for spline smoothing. First, smoothing parameters can be estimated by minimizing criteria that approximate the average mean-squared error of the regression function estimator. Second, the maximum likelihood paradigm can be employed, under the assumption that the regression function is a realization of some stochastic process. The asymptotic properties of both smoothing parameter estimators for penalized splines are studied and compared. A simulation study and a real data example illustrate the theoretical findings.
Original languageEnglish
Pages (from-to)725-741
Number of pages17
JournalJournal of the Royal Statistical Society B: Statistical Methodology
Volume75
Issue number4
Early online dateMar 2013
Publication statusPublished - Sep 2013
Externally publishedYes

Austrian Fields of Science 2012

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