Exact Sample Size Methods for Estimating Parameters of Discrete Distributions

In this paper, we develop an approach for the exact determination of the minimum sample size for estimating the parameter of an integer-valued random variable, which is parameterized by its expectation. Under some continuity and unimodal property assumptions, the exact computation is accomplished by reducing infinite many evaluations of coverage probability to finite many evaluations. Such a reduction is based on our discovery that the minimum of coverage probability with respect to the parameter bounded in an interval is attained at a discrete set of finite many values.