On AR parameter estimation with alpha stable innovations

Shay Maymon, Jonathan Friedmann, Hagit Messer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Several methods have been suggested for estimating the parameters of an auto-regressive (AR) process where the innovation process is an independent, identically distributed (IID) α-stable process. The performance of the proposed algorithms has been studied by simulations. We suggest a novel, maximum likelihood (ML) type method for the same problem. Actually, we suggest use of the ML estimator for the Cauchy distribution for any 1 ≤ α <2. The performance of the proposed method is studied by simulations and its superiority over the existing methods is demonstrated. The simulations have been carried out carefully so the stationarity of the resulting AR process is guaranteed.

Original languageEnglish
Title of host publicationProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages237-240
Number of pages4
ISBN (Electronic)0769501400, 9780769501406
DOIs
StatePublished - 1999
Event1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999 - Caesarea, Israel
Duration: 14 Jun 199916 Jun 1999

Publication series

NameProceedings of the IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999

Conference

Conference1999 IEEE Signal Processing Workshop on Higher-Order Statistics, SPW-HOS 1999
Country/TerritoryIsrael
CityCaesarea
Period14/06/9916/06/99

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