Paper Info
Title
A Comparison of Regression Spline Smoothing Procedures
Abstract
Summary Regression spline smoothing involves modelling a regression function as a piecewise polynomial with a high number of pieces relative to the sample size. Because the number of possible models is so large, efficient strategies for choosing among them are required. In this paper we review approaches to this problem and compare them through a simulation study. For simplicity and conciseness we restrict attention to the univariate smoothing setting with Gaussian noise and the truncated polynomial regression spline basis.
Year
DOI
Venue
2000
10.1007/s001800000047
Computational Statistics
Keywords
DocType
Volume
Keywords: Bayesian variable selection, B-spline, Gibbs sampling, Non-parametric regression, Polynomial spline, Roughness penalty, Stepwise regression.
Journal
15
Issue
ISSN
Citations 
4
0943-4062
13
PageRank 
References 
Authors
3.36
0
1
Name
Order
Citations
PageRank
M. P. Wand15110.35