Dealing with collinearity in FIR models using multiscale estimation

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

1 Citation (Scopus)

Abstract

In this paper, multiscale representation of data is utilized to reduce the collinearity problem often encountered in Finite Impulse Response (FIR) modeling. The idea is to decompose the input-output data at multiple scales, use the scaled signal approximations of the data to construct a FIR model at each scale, and then select among all scales the optimum estimated FIR model. The rationale behind this approach is that the number of significant cross correlation function (CCF) coefficients estimated using the scaled signal approximations of the input-output data decreases at coarser scales. This means that more parsimonious FIR models, with less collinearity and improved estimation accuracy, can be constructed at coarser scales. Of course, the estimation accuracy will deteriorate at very coarse scales. Therefore, it is very important to select the most appropriate scale for modeling purposes, which can be done by selecting the scale which results in the maximum prediction signal to noise ratio. The developed multiscale FIR modeling approach is shown to outperform existing methods, such as ordinary least squares (OLS) regression and ridge regression (RR).

Original languageEnglish
Title of host publicationProceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
Pages8162-8167
Number of pages6
Volume2005
DOIs
Publication statusPublished - 1 Dec 2005
Externally publishedYes
Event44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05 - Seville, Spain
Duration: 12 Dec 200515 Dec 2005

Other

Other44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05
CountrySpain
CitySeville
Period12/12/0515/12/05

Fingerprint

Impulse response
Signal to noise ratio

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Nounou, M. (2005). Dealing with collinearity in FIR models using multiscale estimation. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05 (Vol. 2005, pp. 8162-8167). [1583483] https://doi.org/10.1109/CDC.2005.1583483

Dealing with collinearity in FIR models using multiscale estimation. / Nounou, Mohamed.

Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05. Vol. 2005 2005. p. 8162-8167 1583483.

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

Nounou, M 2005, Dealing with collinearity in FIR models using multiscale estimation. in Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05. vol. 2005, 1583483, pp. 8162-8167, 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05, Seville, Spain, 12/12/05. https://doi.org/10.1109/CDC.2005.1583483
Nounou M. Dealing with collinearity in FIR models using multiscale estimation. In Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05. Vol. 2005. 2005. p. 8162-8167. 1583483 https://doi.org/10.1109/CDC.2005.1583483
Nounou, Mohamed. / Dealing with collinearity in FIR models using multiscale estimation. Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05. Vol. 2005 2005. pp. 8162-8167
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