Best linear prediction for [alpha]-stable random processes
Mohammad Mohammadi and
Adel Mohammadpour
Statistics & Probability Letters, 2009, vol. 79, issue 21, 2266-2272
Abstract:
The best linear prediction for [alpha]-stable random processes based on some past values is presented. The prediction is the best with respect to a criterion known as stable covariation. The minimum stable covariations can be considered as the smallest error tail probabilities. The predictor obtained is equal to the best linear predictor based on minimization of second-moment error for Gaussian processes.
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167-7152(09)00288-0
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:stapro:v:79:y:2009:i:21:p:2266-2272
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01
Access Statistics for this article
Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul
More articles in Statistics & Probability Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().