Electronic Communications in Probability

Optimising prediction error among completely monotone covariance sequences

Ross McVinish

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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

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

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

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

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

aggregation maximum entropy moment space

This work is licensed under aCreative Commons Attribution 3.0 License.


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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