Electronic Communications in Probability

Optimising prediction error among completely monotone covariance sequences

Ross McVinish

Full-text: Open access

Abstract

We provide a characterisation of Gaussian time series which optimise the one-step prediction error subject to the covariance sequence being completely monotone with the first $m$ covariances specified.

Article information

Source
Electron. Commun. Probab., Volume 13 (2008), paper no. 11, 113-120.

Dates
Accepted: 2 March 2008
First available in Project Euclid: 6 June 2016

Permanent link to this document
https://projecteuclid.org/euclid.ecp/1465233438

Digital Object Identifier
doi:10.1214/ECP.v13-1355

Mathematical Reviews number (MathSciNet)
MR2386067

Zentralblatt MATH identifier
1191.60049

Subjects
Primary: 60G25: Prediction theory [See also 62M20]
Secondary: 44A60: Moment problems

Keywords
aggregation maximum entropy moment space

Rights
This work is licensed under aCreative Commons Attribution 3.0 License.

Citation

McVinish, Ross. Optimising prediction error among completely monotone covariance sequences. Electron. Commun. Probab. 13 (2008), paper no. 11, 113--120. doi:10.1214/ECP.v13-1355. https://projecteuclid.org/euclid.ecp/1465233438


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