Prediction and Non-Gaussian Autoregressive Stationary Sequences

Murray Rosenblatt

doi:10.1214/aoap/1177004838

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Home > Journals > Ann. Appl. Probab. > Volume 5 > Issue 1 > Article

February, 1995 Prediction and Non-Gaussian Autoregressive Stationary Sequences

Murray Rosenblatt

Ann. Appl. Probab. 5(1): 239-247 (February, 1995). DOI: 10.1214/aoap/1177004838

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Abstract

The object of this paper is to show that under certain auxiliary assumptions a stationary autoregressive sequence has a best predictor in mean square that is linear if and only if the sequence is minimum phase or is Gaussian when all moments are finite.

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Murray Rosenblatt. "Prediction and Non-Gaussian Autoregressive Stationary Sequences." Ann. Appl. Probab. 5 (1) 239 - 247, February, 1995. https://doi.org/10.1214/aoap/1177004838

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Published: February, 1995

First available in Project Euclid: 19 April 2007

zbMATH: 0828.62084

MathSciNet: MR1325051

Digital Object Identifier: 10.1214/aoap/1177004838

Subjects:

Primary: 60G25

Secondary: 10J10 , 60G10 , 62M20

Keywords: Autoregressive sequence , non-Gaussian , nonlinear prediction , non-minimum phase

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Ann. Appl. Probab.

Vol.5 • No. 1 • February, 1995

Institute of Mathematical Statistics

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Murray Rosenblatt "Prediction and Non-Gaussian Autoregressive Stationary Sequences," The Annals of Applied Probability, Ann. Appl. Probab. 5(1), 239-247, (February, 1995)

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