Penalized partial least squares based on B-Splines transformations

Arbeitspapier

We propose a novel method to model nonlinear regression problems by adapting the principle of penalization to Partial Least Squares (PLS). Starting with a generalized additive model, we expand the additive component of each variable in terms of a generous amount of B-Splines basis functions. In order to prevent overfitting and to obtain smooth functions, we estimate the regression model by applying a penalized version of PLS. Although our motivation for penalized PLS stems from its use for B-Splines transformed data, the proposed approach is very general and can be applied to other penalty terms or to other dimension reduction techniques. It turns out that penalized PLS can be computed virtually as fast as PLS. We prove a close connection of penalized PLS to the solution of preconditioned linear systems. In the case of high-dimensional data, the new method is shown to be an attractive competitor to other techniques for estimating generalized additive models. If he number of predictor variables is high compared to the number of examples, traditional techniques often suffer from overfitting. We illustrate that penalized PLS performs well in these situations. - generalized additive model ; dimension reduction ; nonlinear regression ; conjugate gradient

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Discussion Paper ; No. 485

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Krämer, Nicole ; Boulesteix, Anne-Laure ; Tutz, Gerhard
• Event: Veröffentlichung
• (who): Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen
• (where): München
• (when): 2006

Further information

• DOI: doi:10.5282/ubm/epub.1853
• Handle: http://hdl.handle.net/10419/31057
• URN: urn:nbn:de:bvb:19-epub-1853-2
• Last update: 10.03.2025, 11:45 AM CET