Set estimation refers to the process of constructing a subset of the parameter space, based on observed data from a probability distribution. The subset will contain the true value of the parameter with a specified confidence level. In this chapter, we explore the basic method of set estimation using pivot variables. We study set estimation in some of the most important models: the single variable normal model, the two-variable normal model, and the Bernoulli model.
A great discovery solves a great problem but there is a grain of discovery in the solution of any problem.—George Polya