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Teoriya Veroyatnostei i ee Primeneniya, 1994, Volume 39, Issue 2, Pages 294–312 (Mi tvp3803)  
This article is cited in 10 scientific papers (total in 10 papers)

On prediction of heavy-tailed autoregressive sequences: forward versus reversed time

S. Cambanisa, I. Fakhre-Zakeriba

a University of North Carolina, Chapel Hill, NC
b University of Maryland, College Park, MD
Full-text PDF (943 kB) Citations (10)
Abstract: The prediction of heavy-tailed autoregressive sequences of first order is considered. Necessary and sufficient conditions are obtained for the linearity of the regression predictor with time reversed, which answers a question raised in [13]. Also, the error of the linear regression predictor is compared with that of the best linear predictor in the symmetric stable case.
Keywords: autoregressive processes linear prediction, regression, stable and semistable distributions.
Received: 11.03.1993
English version:
Theory of Probability and its Applications, 1994, Volume 39, Issue 2, Pages 217–233
DOI: https://doi.org/10.1137/1139013
Bibliographic databases:
Document Type: Article
Language: English
Citation: S. Cambanis, I. Fakhre-Zakeri, “On prediction of heavy-tailed autoregressive sequences: forward versus reversed time”, Teor. Veroyatnost. i Primenen., 39:2 (1994), 294–312; Theory Probab. Appl., 39:2 (1994), 217–233
Citation in format AMSBIB
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\paper On prediction of heavy-tailed autoregressive sequences: forward versus reversed time
\jour Teor. Veroyatnost. i Primenen.
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\issue 2
\pages 294--312
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\zmath{https://zbmath.org/?q=an:0834.62088|0827.62090}
\transl
\jour Theory Probab. Appl.
\yr 1994
\vol 39
\issue 2
\pages 217--233
\crossref{https://doi.org/10.1137/1139013}
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  • https://www.mathnet.ru/eng/tvp/v39/i2/p294
    • This publication is cited in the following 10 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Теория вероятностей и ее применения Theory of Probability and its Applications
     
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