Lower Bound on the Mean-Square Error in Random Parameter Estimation

摘要

A new lower bound on mean square error in parameter estimation is presented. The bound is tighter than the Cramer 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. (Reprints).