2010/07/24 by Xinjia Chen, Chen, Xinjia
Computer Science · Decision Sciences · Mathematics · #Advanced Statistical Process Monitoring #Adversarial Robustness in Machine Learning #FOS: Mathematics #Probability (math.PR) #Statistical Methods in Clinical Trials #Statistics Theory (math.ST) #math.PR #math.ST #stat.TH
paper · pdf · doi:10.48550/arxiv.1007.4278
23 pages, no figure; added more applications
openalex publication_date 2010/07/24 · arxiv created 2012/02/09 · arxiv updated 2012/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.