The error variance of the optimal linear smoother and maximum-variance fractional pole models

Tryphon T. Georgiou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The variance of the optimal one-step ahead linear prediction error of a discrete-time stationary stochastic process is given by the well-known Szegö-Kolmogorov formula as the geometric mean of the spectral density function. We first derive an analogous expression for the optimal linear smoother which uses the infinite past and the infinite future to determine the present. The least variance turns out to be the harmonic mean of the spectral density function. Building on this, we explore the question of what is the most random power spectrum in the sense of corresponding to the largest variance optimal linear smoother (i.e., least "smoothable"), which is consistent with finitely many covariance moments. It turns out that it can be described by an all-pole model, albeit the poles are fractional.

Original languageEnglish (US)
Title of host publicationProceedings of the 45th IEEE Conference on Decision and Control 2006, CDC
Pages1685-1691
Number of pages7
StatePublished - Dec 1 2006
Event45th IEEE Conference on Decision and Control 2006, CDC - San Diego, CA, United States
Duration: Dec 13 2006Dec 15 2006

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other45th IEEE Conference on Decision and Control 2006, CDC
Country/TerritoryUnited States
CitySan Diego, CA
Period12/13/0612/15/06

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