Krivobokova, Tatyana

[["dc.bibliographiccitation.firstpage","725"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Journal of the Royal Statistical Society: Series B (Statistical Methodology)"],["dc.bibliographiccitation.lastpage","741"],["dc.bibliographiccitation.volume","75"],["dc.contributor.author","Krivobokova, Tatyana"],["dc.date.accessioned","2017-09-07T11:50:06Z"],["dc.date.available","2017-09-07T11:50:06Z"],["dc.date.issued","2013"],["dc.description.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."],["dc.identifier.doi","10.1111/rssb.12010"],["dc.identifier.gro","3145853"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/3584"],["dc.notes.intern","mathe"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.publisher","Wiley-Blackwell"],["dc.relation.issn","1369-7412"],["dc.subject","Average mean-squared error minimizer Maximum likelihood Oracle parameters"],["dc.title","Smoothing parameter selection in two frameworks for penalized splines"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]