A lower bound on the meansquare error in random parameter estimation

Anthony J. Weiss; Ehud Weinstein

doi:10.1109/9780470544198.ch8

A lower bound on the meansquare error in random parameter estimation

Anthony J. Weiss, Ehud Weinstein

School of Electrical Engineering

Woods Hole Oceanographic Institution

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

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 language	English
Title of host publication	Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
Publisher	Wiley-IEEE Press
Pages	163-165
Number of pages	3
ISBN (Electronic)	9780470544198
ISBN (Print)	0470120959, 9780470120958
DOIs	https://doi.org/10.1109/9780470544198.ch8
State	Published - 1 Jan 2007

Keywords

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

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10.1109/9780470544198.ch8

Cite this

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KW - Signal to noise ratio

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A lower bound on the meansquare error in random parameter estimation

Abstract

Keywords

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