The Annals of Statistics

The Average Posterior Variance of a Smoothing Spline and a Consistent Estimate of the Average Squared Error

Douglas Nychka

Full-text: Open access

Abstract

A smoothing spline estimator can be interpreted in two ways: either as the solution to a variational problem or as the posterior mean when a particular Gaussian prior is placed on the unknown regression function. In order to explain the remarkable performance of her Bayesian "confidence intervals" in a simulation study, Wahba conjectured that the average posterior variance of a spline estimate evaluated at the observation points will be close to the expected average squared error. The estimate of the average posterior variance proposed by Wahba is shown to converge in probability to a quantity proportional to the expected average squared error. This result is established by relating this statistic to a consistent risk estimate based on generalized cross-validation.

Article information

Source
Ann. Statist. Volume 18, Number 1 (1990), 415-428.

Dates
First available: 12 April 2007

Permanent link to this document
http://projecteuclid.org/euclid.aos/1176347508

JSTOR
links.jstor.org

Digital Object Identifier
doi:10.1214/aos/1176347508

Mathematical Reviews number (MathSciNet)
MR1041401

Zentralblatt MATH identifier
0731.62084

Subjects
Primary: 62G05: Estimation
Secondary: 62G15: Tolerance and confidence regions

Keywords
Nonparametric regression smoothing spline confidence intervals risk estimate cross-validation

Citation

Nychka, Douglas. The Average Posterior Variance of a Smoothing Spline and a Consistent Estimate of the Average Squared Error. The Annals of Statistics 18 (1990), no. 1, 415--428. doi:10.1214/aos/1176347508. http://projecteuclid.org/euclid.aos/1176347508.


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