Nonparametric prediction for random fields

Madan L. Puri and Frits H. Ruymgaart

Stochastic Processes and their Applications, 1993, vol. 48, issue 1, 139-156

Abstract: We study prediction for vector valued random fields in a nonparametric setting. The prediction problem is formulated as the problem if estimating certain conditional expectations and a speed of uniform a.s. convergence is obtained, modifying results for conditional empirical processes derived from series with one-dimensional time. As an alternative to the usual mixing conditions we model the dependence by asymptotic decomposability. This includes linear (which generalizes ARMA) fields and random fields with a finite order Volterra expansion. As an example of a linear field we briefly discuss the finite-differences simulation of the heat equation blurred by additive random noise.

Keywords: random; fields; prediction; conditional; expectations; asymptotic; decomposability (search for similar items in EconPapers)
Date: 1993
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/0304-4149(93)90111-G
Full text for ScienceDirect subscribers only

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:eee:spapps:v:48:y:1993:i:1:p:139-156

Ordering information: This journal article can be ordered from
http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().