Estimating the smoothing parameter in generalized spline-based regression

Angelika Linde
Additional contact information
Angelika Linde: University of Bremen

Computational Statistics, 2001, vol. 16, issue 1, No 4, 73-95

Abstract: Summary Smoothing with splines requires a smoothing parameter which is most often obtained by cross-validation. Interpreting splines from a Bayesian point of view this is an empirical Bayesian approach. A fully Bayesian approach with a (hyper-) prior for the smoothing parameter is computationally more demanding even for Gaussian data and really accessible only using simulation methods. Smoothing in generalized regression models is presented in a Bayesian interpretation and tried with Gaussian and binary data using the implementation of Gibbs sampling in BUGS. The results are compared to those obtained by cross-validation. The approach essentially does work but convergence of just the smoothing parameter turns out to be crucial. The sensitivity of the estimated function values w.r.t. to the prior is satisfactory.

Keywords: nonparametric regression; hierarchical generalized regression model; splines; Gibbs sampling (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s001800100052 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:16:y:2001:i:1:d:10.1007_s001800100052

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s001800100052

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().