Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If you need an accessible version of this item please contact JSTOR User Support

Bayesian Regression with Multivariate Linear Splines

C. C. Holmes and B. K. Mallick
Journal of the Royal Statistical Society. Series B (Statistical Methodology)
Vol. 63, No. 1 (2001), pp. 3-17
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/2680630
Page Count: 15
  • Get Access
  • Read Online (Free)
  • Download ($29.00)
  • Cite this Item
If you need an accessible version of this item please contact JSTOR User Support
Bayesian Regression with Multivariate Linear Splines
Preview not available

Abstract

We present a Bayesian analysis of a piecewise linear model constructed using basis functions which generalizes the univariate linear spline to higher dimensions. Prior distributions are adopted on both the number and the locations of the splines, which leads to a model averaging approach to prediction with predictive distributions that take into account model uncertainty. Conditioning on the data produces a Bayes local linear model with distributions on both predictions and local linear parameters. The method is spatially adaptive and covariate selection is achieved by using splines of lower dimension than the data.

Page Thumbnails

  • Thumbnail: Page 
[3]
    [3]
  • Thumbnail: Page 
4
    4
  • Thumbnail: Page 
5
    5
  • Thumbnail: Page 
6
    6
  • Thumbnail: Page 
7
    7
  • Thumbnail: Page 
8
    8
  • Thumbnail: Page 
9
    9
  • Thumbnail: Page 
10
    10
  • Thumbnail: Page 
11
    11
  • Thumbnail: Page 
12
    12
  • Thumbnail: Page 
13
    13
  • Thumbnail: Page 
14
    14
  • Thumbnail: Page 
15
    15
  • Thumbnail: Page 
16
    16
  • Thumbnail: Page 
17
    17