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Estimator selection: End-performance metric aspects
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0003-0355-2663
2015 (English)In: Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers , 2015, 4430-4435 p.Conference paper, Published paper (Refereed)
Abstract [en]

Recently, a framework for application-oriented optimal experiment design has been introduced. In this context, the distance of the estimated system from the true one is measured in terms of a particular end-performance metric. This treatment leads to superior unknown system estimates to classical experiment designs based on usual pointwise functional distances of the estimated system from the true one. The separation of the system estimator from the experiment design is done within this new framework by choosing and fixing the estimation method to either a maximum likelihood (ML) approach or a Bayesian estimator such as the minimum mean square error (MMSE). Since the MMSE estimator delivers a system estimate with lower mean square error (MSE) than the ML estimator for finite-length experiments, it is usually considered the best choice in practice in signal processing and control applications. Within the application-oriented framework a related meaningful question is: Are there endperformance metrics for which the ML estimator outperforms the MMSE when the experiment is finite-length? In this paper, we affirmatively answer this question based on a simple linear Gaussian regression example.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers , 2015. 4430-4435 p.
Keyword [en]
Estimation, Maximum likelihood, Maximum likelihood estimation, Signal processing, Application-oriented, Bayesian estimators, Control applications, Gaussian regression, Maximum likelihood approaches, Minimum mean square errors (MMSE), Optimal experiment design, Performance metrices, Mean square error
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-176133DOI: 10.1109/ACC.2015.7172026ISI: 000370259204088Scopus ID: 2-s2.0-84940905591ISBN: 9781479986842 (print)OAI: oai:DiVA.org:kth-176133DiVA: diva2:875894
Conference
2015 American Control Conference, ACC 2015, 1 July 2015 through 3 July 2015
Note

QC 20151202

Available from: 2015-12-02 Created: 2015-11-02 Last updated: 2016-03-29Bibliographically approved

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Rojas, Cristian R.

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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  • asciidoc
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