LOWER BOUND ON THE MEAN-SQUARE ERROR IN RANDOM PARAMETER ESTIMATION.

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Abstract

A new lower bound on mean-square error in parameter estimation is presented. The bound is tighter than the Cramer-Rao and Bobrovsky-Zakai (1970) 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 parameter.

Original languageEnglish
Pages (from-to)680-682
Number of pages3
JournalIEEE Transactions on Information Theory
VolumeIT-31
Issue number5
DOIs
StatePublished - 1985

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