A lower bound on the meansquare error in random parameter estimation

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


A new lower bound on mean-square error in parameter estimation is presented. The bound is tighter than the Cramér-Rao and Bobrovsky-Zakai lower bounds. It requires no bias or regularity assumptions, it is computationally simple, and it can be applied to estimates of vector parameters or functions of the parameters.

Original languageEnglish
Title of host publicationBayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
PublisherWiley-IEEE Press
Number of pages3
ISBN (Electronic)9780470544198
ISBN (Print)0470120959, 9780470120958
StatePublished - 1 Jan 2007


  • Algebra
  • Covariance matrix
  • Estimation error
  • Indium tin oxide
  • Linear matrix inequalities
  • Parameter estimation
  • Signal to noise ratio


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