Sequential Tests of Statistical Hypotheses with Confidence Limits

TL;DR

This paper introduces a flexible sequential testing framework using confidence limits, which improves efficiency over traditional methods like the SPRT, especially for complex hypotheses such as differences in binomial proportions.

Contribution

It presents a novel general method for sequential hypothesis testing based on confidence limits, including an inclusion principle for multistage plans, enhancing efficiency and applicability.

Findings

Proposed a confidence limit-based sequential testing method.

Developed an inclusion principle for multistage testing.

Demonstrated improved efficiency over SPRT.

Abstract

In this paper, we propose a general method for testing composite hypotheses. Our idea is to use confidence limits to define stopping and decision rules. The requirements of operating characteristic function can be satisfied by adjusting the coefficients of the confidence limits. For common distributions, such adjustment can be done via efficient computation by making use of the monotonicity of the associated operating characteristic function. We show that the problem of testing multiple hypotheses can be cast into the general framework of constructing sequential random intervals with prescribed coverage probabilities. We propose an inclusion principle for constructing multistage testing plans. It is demonstrated that our proposed testing plans can be substantially more efficient than the sequential probability ratio test and its variations. We apply our general methodology to develop an exact approach for testing hypotheses regarding the difference of two binomial proportions.