Empirical Bayesian Finite Impulse Response Modeling

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

Abstract

This paper presents a Bayesian modeling technique, called Empirical Bayesian Finite Response (EBFIR) modeling, that helps deal with the collinearity problem usually encountered in FIR models, and helps improve the estimation accuracy of their coefficients. The developed technique iteratively solves for the prior density used in estimation and the FIR coefficients. The advantages of the developed EBFIR modeling technique are also illustrated though a simulated example.

Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Decision and Control
Pages6480-6484
Number of pages5
Volume6
DOIs
Publication statusPublished - 1 Dec 2003
Externally publishedYes
Event42nd IEEE Conference on Decision and Control - Maui, HI, United States
Duration: 9 Dec 200312 Dec 2003

Other

Other42nd IEEE Conference on Decision and Control
CountryUnited States
CityMaui, HI
Period9/12/0312/12/03

Fingerprint

Impulse Response
Impulse response
Modeling
Collinearity
Bayesian Modeling
Coefficient
Model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

Cite this

Nounou, M. (2003). Empirical Bayesian Finite Impulse Response Modeling. In Proceedings of the IEEE Conference on Decision and Control (Vol. 6, pp. 6480-6484) https://doi.org/10.1109/CDC.2003.1272387

Empirical Bayesian Finite Impulse Response Modeling. / Nounou, Mohamed.

Proceedings of the IEEE Conference on Decision and Control. Vol. 6 2003. p. 6480-6484.

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

Nounou, M 2003, Empirical Bayesian Finite Impulse Response Modeling. in Proceedings of the IEEE Conference on Decision and Control. vol. 6, pp. 6480-6484, 42nd IEEE Conference on Decision and Control, Maui, HI, United States, 9/12/03. https://doi.org/10.1109/CDC.2003.1272387
Nounou M. Empirical Bayesian Finite Impulse Response Modeling. In Proceedings of the IEEE Conference on Decision and Control. Vol. 6. 2003. p. 6480-6484 https://doi.org/10.1109/CDC.2003.1272387
Nounou, Mohamed. / Empirical Bayesian Finite Impulse Response Modeling. Proceedings of the IEEE Conference on Decision and Control. Vol. 6 2003. pp. 6480-6484
@inproceedings{6cd5d62012fe439498317f103ce2b02d,
title = "Empirical Bayesian Finite Impulse Response Modeling",
abstract = "This paper presents a Bayesian modeling technique, called Empirical Bayesian Finite Response (EBFIR) modeling, that helps deal with the collinearity problem usually encountered in FIR models, and helps improve the estimation accuracy of their coefficients. The developed technique iteratively solves for the prior density used in estimation and the FIR coefficients. The advantages of the developed EBFIR modeling technique are also illustrated though a simulated example.",
author = "Mohamed Nounou",
year = "2003",
month = "12",
day = "1",
doi = "10.1109/CDC.2003.1272387",
language = "English",
isbn = "0780379241",
volume = "6",
pages = "6480--6484",
booktitle = "Proceedings of the IEEE Conference on Decision and Control",

}

TY - GEN

T1 - Empirical Bayesian Finite Impulse Response Modeling

AU - Nounou, Mohamed

PY - 2003/12/1

Y1 - 2003/12/1

N2 - This paper presents a Bayesian modeling technique, called Empirical Bayesian Finite Response (EBFIR) modeling, that helps deal with the collinearity problem usually encountered in FIR models, and helps improve the estimation accuracy of their coefficients. The developed technique iteratively solves for the prior density used in estimation and the FIR coefficients. The advantages of the developed EBFIR modeling technique are also illustrated though a simulated example.

AB - This paper presents a Bayesian modeling technique, called Empirical Bayesian Finite Response (EBFIR) modeling, that helps deal with the collinearity problem usually encountered in FIR models, and helps improve the estimation accuracy of their coefficients. The developed technique iteratively solves for the prior density used in estimation and the FIR coefficients. The advantages of the developed EBFIR modeling technique are also illustrated though a simulated example.

UR - http://www.scopus.com/inward/record.url?scp=1542378189&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1542378189&partnerID=8YFLogxK

U2 - 10.1109/CDC.2003.1272387

DO - 10.1109/CDC.2003.1272387

M3 - Conference contribution

AN - SCOPUS:1542378189

SN - 0780379241

VL - 6

SP - 6480

EP - 6484

BT - Proceedings of the IEEE Conference on Decision and Control

ER -