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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/269

Title: Some theory for penalized spline additive models
Authors: AERTS, Marc
Wand, Matthew P.
Keywords: Non and semiparametric methods
Mathematical Statistics
Issue Date: 2002
Citation: Journal of Statistical Planning and Inference, 103(1-2). p. 455-470
Abstract: Generalized additive models have become one of the most widely used modern statistical tools. Traditionally, they are fit through scatterplot smoothing and the backfitting algorithm. However, a more recent development is the direct fitting through the use of low-rank smoothers (Hastie, J. Roy. Statist. Soc. Ser. B 58 (1996) 379). A particularly attractive example of this is through use of penalized splines (Marx and Eilers, Comput. Statist. Data Anal. 28 (1998) 193). Such an approach has a number of advantages, particularly regarding computation. In this paper, we exploit the explicitness of penalized spline additive models to derive some useful and revealing theoretical approximations.
URI: http://hdl.handle.net/1942/269
DOI: 10.1016/S0378-3758(01)00237-3
ISI #: 000175149800028
ISSN: 0378-3758
Category: A1
Type: Journal Contribution
Validation: ecoom, 2003
Appears in Collections: Research publications

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