The Annals of Statistics

Prediction and Design

Donald Ylvisaker

Full-text: Open access

Abstract

In various settings, the observation of a stochastic process at a finite number of locations leads to natural prediction and design questions. General problems of this type are introduced and then related to specific areas of application. A class of processes called G-MAPs is studied with reference to their predictive and other behavior. These processes include many familiar ones and, through being tied to Markov processes, allow a fresh view of prediction. Among other things, G-MAPs stand as reasonably workable possibilities for Bayesian priors in some complex contexts.

Article information

Source
Ann. Statist., Volume 15, Number 1 (1987), 1-19.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176350247

Digital Object Identifier
doi:10.1214/aos/1176350247

Mathematical Reviews number (MathSciNet)
MR885721

Zentralblatt MATH identifier
0646.62080

JSTOR
links.jstor.org

Subjects
Primary: 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11]
Secondary: 62K05: Optimal designs 60G15: Gaussian processes

Keywords
Prediction design Markov processes Gaussian fields Markov property Bayesian models

Citation

Ylvisaker, Donald. Prediction and Design. Ann. Statist. 15 (1987), no. 1, 1--19. doi:10.1214/aos/1176350247. https://projecteuclid.org/euclid.aos/1176350247


Export citation