A lower bound on the mean-square error in random parameter estimation (Corresp.)

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.