Publication
Title
P-splines regression smoothing and difference type of penalty
Author
Abstract
P-splines regression provides a flexible smoothing tool. In this paper we consider difference type penalties in a context of nonparametric generalized linear models, and investigate the impact of the order of the differencing operator. Minimizing Akaikes information criterion we search for a possible best data-driven value of the differencing order. Theoretical derivations are established for the normal model and provide insights into a possible optimal choice of the differencing order and its interrelation with other parameters. Applications of the selection procedure to non-normal models, such as Poisson models, are given. Simulation studies investigate the performance of the selection procedure and we illustrate its use on real data examples.
Language
English
Source (journal)
Statistics and computing. - London
Publication
London : 2010
ISSN
0960-3174
Volume/pages
20:4(2010), p. 499-511
ISI
000281983200009
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Faculty/Department
Publication type
Subject
External links
Web of Science
Record
Identification
Creation 14.03.2012
Last edited 01.11.2017