Approximating data with weighted smoothing splines

dc.contributor.author	Davies, P. L.
dc.contributor.author	Meise, M.
dc.date.accessioned	2005-12-14T09:10:34Z
dc.date.available	2005-12-14T09:10:34Z
dc.date.issued	2005-12-14T09:10:34Z
dc.description.abstract	Given a data set (t_i, y_i), i = 1,... ,n with the t_i ∈ [0, 1] non-parametric regression is concerned with the problem of specifying a suitable function f_n : [0, 1] → R such that the data can be reasonably approximated by the points (t_i, f_n(t_i)), i = 1,... ,n. A common desideratum is that the function fn be smooth but the path towards this goal is often the indirect one of assuming a “true” data generating function f and then measuring performance by the expected mean square. The approach taken in this paper is a different one. We specify precisely what we mean by a function fn being an adequate approximation to the data and then, using weighted splines, we try to maximize the smoothness given the approximation constraints.	de
dc.format.extent	2738906 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/21759
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15378
dc.language.iso	de
dc.subject	Approximation	de
dc.subject	Non-parametric regression	de
dc.subject	Residuals	de
dc.subject	Smoothing Splines	de
dc.subject	Thin Plate Splines	de
dc.subject.ddc	004
dc.title	Approximating data with weighted smoothing splines	de
dc.type	Text
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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