Computational Methods for Evaluating Sequential Tests and Post-test Estimation via the Sufficiency Principle

By the sufficiency principle, the probability density of a sequential test statistic under certain conditions can be factored into a known function that does not depend on the stopping rule and a conditional probability that is free of unknown parameters. We develop general theorems and propose a unified approach to analyzing and evaluating various properties of sequential tests and post-test estimation. The proposed approach is of practical value since it allows for effective evaluation of properties of special interest, such as the bias-adjustment of post-test estimation after a sequential test, and the probability of discordance between a sequential test and a nonsequential test.