ABSTRACT
In the previous two chapters (Chapters 12 and 13) we learnt the general concepts of estimators and the means to analyze their goodness. An important message from those chapters is that there exists more than one way to estimate the parameters (unknowns). Each estimator differs in the objective function and the assumptions that it makes on the data, which in turn have a direct impact on the quality of the estimates. The C-R inequality states the conditions under which an efficient estimator can be found. In the least, we should be using a best linear unbiased estimator. Chapter 12 showed how different objectives or estimation cost functions lead to different forms of estimators with varying solutions and properties. In principle, there exist innumerable methods for estimation. However, it suffices to study four classes of methods considering their wide usage, universal appeal and that most of the existing methods can be cast into one of these forms.
