Universal linear least-squares prediction in the presence of noise

Georg C. Zeitler; Andrew Carl Singer

doi:10.1109/SSP.2007.4301331

Universal linear least-squares prediction in the presence of noise

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Universal linear least squares prediction of real-valued bounded individual sequences in the presence of additive bounded noise is considered. It is shown that there is a sequential predictor observing noisy samples of the sequence to be predicted only, whose loss in terms of the noise-free sequence is asymptotically as small as that of the best batch predictor out of the class of all linear predictors with knowledge of the entire noisy sequence in advance.

Original language	English (US)
Title of host publication	2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings
Pages	611-614
Number of pages	4
DOIs	https://doi.org/10.1109/SSP.2007.4301331
State	Published - 2007
Event	2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007 - Madison, WI, United States Duration: Aug 26 2007 → Aug 29 2007

Other

Other	2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007
Country/Territory	United States
City	Madison, WI
Period	8/26/07 → 8/29/07

Keywords

Least squares
Linear
Noise
Prediction

ASJC Scopus subject areas

Signal Processing

Online availability

10.1109/SSP.2007.4301331

Library availability

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title = "Universal linear least-squares prediction in the presence of noise",

abstract = "Universal linear least squares prediction of real-valued bounded individual sequences in the presence of additive bounded noise is considered. It is shown that there is a sequential predictor observing noisy samples of the sequence to be predicted only, whose loss in terms of the noise-free sequence is asymptotically as small as that of the best batch predictor out of the class of all linear predictors with knowledge of the entire noisy sequence in advance.",

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author = "Zeitler, {Georg C.} and Singer, {Andrew Carl}",

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doi = "10.1109/SSP.2007.4301331",

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pages = "611--614",

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AU - Zeitler, Georg C.

AU - Singer, Andrew Carl

PY - 2007

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AB - Universal linear least squares prediction of real-valued bounded individual sequences in the presence of additive bounded noise is considered. It is shown that there is a sequential predictor observing noisy samples of the sequence to be predicted only, whose loss in terms of the noise-free sequence is asymptotically as small as that of the best batch predictor out of the class of all linear predictors with knowledge of the entire noisy sequence in advance.

KW - Least squares

KW - Linear

KW - Noise

KW - Prediction

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U2 - 10.1109/SSP.2007.4301331

DO - 10.1109/SSP.2007.4301331

M3 - Conference contribution

AN - SCOPUS:47849131916

SN - 142441198X

SN - 9781424411986

SP - 611

EP - 614

BT - 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, SSP 2007, Proceedings

T2 - 2007 IEEE/SP 14th WorkShoP on Statistical Signal Processing, SSP 2007

Y2 - 26 August 2007 through 29 August 2007

ER -

Universal linear least-squares prediction in the presence of noise

Abstract

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Keywords

ASJC Scopus subject areas

Online availability

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