Bounded error estimation: A Chebyshev center approach

Yonina C. Eldar, Amir Beck, Marc Teboulle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We develop a nonlinear minimax estimator for the classical linear regression model assuming that the true parameter vector lies in an intersection of ellipsoids. We seek an estimate that minimizes the worst-case estimation error over the given parameter set. Since this problem is intractable, we approximate it using semidefinite relaxation, and refer to the resulting estimate as the relaxed Chebyshev center (RCC). We then demonstrate through simulations that the RCC can significantly improve the estimation error over the conventional constrained least-squares method.

Original languageEnglish
Title of host publication2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Pages205-208
Number of pages4
DOIs
StatePublished - 2007
Event2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP - St. Thomas, Virgin Islands, U.S.
Duration: 12 Dec 200714 Dec 2007

Publication series

Name2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP

Conference

Conference2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMPSAP
Country/TerritoryVirgin Islands, U.S.
CitySt. Thomas
Period12/12/0714/12/07

Keywords

  • Estimation
  • Minimax
  • Regression

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