The Annals of Statistics

A Kiefer-Wolfowitz Theorem in a Stochastic Process Setting

M. C. Spruill and W. J. Studden

Full-text: Open access

Abstract

In the regression design problem with observations which are second order processes the estimation of the mean function involves function space valued random variables. The best unbiased linear estimator of the mean function is found and an exact analogue of the Kiefer-Wolfowitz theorem in design theory is proved.

Article information

Source
Ann. Statist., Volume 7, Number 6 (1979), 1329-1332.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176344850

Digital Object Identifier
doi:10.1214/aos/1176344850

Mathematical Reviews number (MathSciNet)
MR550154

Zentralblatt MATH identifier
0418.62046

JSTOR
links.jstor.org

Subjects
Primary: 62J05: Linear regression
Secondary: 62K05: Optimal designs 62M99: None of the above, but in this section

Keywords
Stochastic process kernel Hilbert space function space-valued estimators $D$-optimum designs minimax designs

Citation

Spruill, M. C.; Studden, W. J. A Kiefer-Wolfowitz Theorem in a Stochastic Process Setting. Ann. Statist. 7 (1979), no. 6, 1329--1332. doi:10.1214/aos/1176344850. https://projecteuclid.org/euclid.aos/1176344850


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