LOWER BOUNDS IN PARAMETER ESTIMATION - SUMMARY OF RESULTS.

The authors formulate a general class of lower bounds on the attainable mean square error (MSE) in parameter estimation, and show that the Cramer-Rao, Bhattacharyya, and Bobrovsky-Zakai lower bounds are special cases in the class. They then produce a lower bound in the class that is often significantly tighter than the abovementioned bounds. The proposed bound is simple to analyze and compute; it is free from bias and regularity assumptions; it readily incorporates a priori information; and it readily generalizes to the estimation of vector parameters and any given function of the parameters.