Introduction to Estimation Theory

This chapter presents an introduction to estimation theory. Although theory of minimum variance unbiased estimation is an elegant framework for nonrandom parameter estimation, in practice, it may be difficult to apply. The chapter explains random parameter estimation, particularly, Bayesian estimation. For unbiased estimators, the information inequality reduces to the so‐called Cramer–Rao lower bound (CRLB). An unbiased estimator that achieves the CRLB is called an efficient estimator. If an estimator only achieves the CRLB asymptotically, then it is called an asymptotically efficient estimator.