Lower bounds in parameter estimation - summary of results

We formulate a general class of lower bounds on the attainable mean square error (MSE) in parameter estimation, and show that the Cramer-Rao, the Bhattacharyya and the Bobrovsky-Zakai lower bounds are special cases in the class. We then propose a lower bound in the class that is often significantly tighter than the above mentioned 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.