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|Title: ||Some theory for penalized spline additive models|
|Authors: ||AERTS, Marc|
Wand, Matthew P.
|Keywords: ||Non and semiparametric methods|
|Issue Date: ||2002|
|Publisher: ||ELSEVIER SCIENCE BV|
|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.|
|ISI #: ||000175149800028|
|Type: ||Journal Contribution|
|Validation: ||ecoom, 2003|
|Appears in Collections: ||Research publications|
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