PREDICTION AND NON-GAUSSIAN AUTOREGRESSIVE STATIONARY SEQUENCES
成果类型：
Article
署名作者：
Rosenblatt, Murray
署名单位：
University of California System; University of California San Diego
刊物名称：
ANNALS OF APPLIED PROBABILITY
ISSN/ISSBN：
1050-5164
DOI：
10.1214/aoap/1177004838
发表日期：
1995
页码：
239-247
关键词：
摘要：
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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