2014/12/04 by Anatoli Juditsky, Arkadi Nemirovski, Juditsky, Anatoli +1
Computer Science · Decision Sciences · Mathematics · #Advanced Statistical Process Monitoring #Algorithm #Computation (stat.CO) #Computer science #Conic optimization #Convex analysis #Convex optimization #Convex set #Extension (predicate logic) #FOS: Computer and information sciences #FOS: Mathematics #Line (geometry) #Machine Learning and Algorithms #Mathematical optimization #Mathematics #Regular polygon #Sequential analysis #Set (abstract data type) #Statistical Methods in Clinical Trials #Statistics #Statistics Theory (math.ST) #math.ST #stat.CO #stat.TH
paper · pdf · doi:10.48550/arxiv.1412.1605
arXiv admin note: substantial text overlap with arXiv:1311.6765
openalex publication_date 2014/12/04 · arxiv created 2017/02/24 · arxiv updated 2017/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a new approach to sequential testing which is an adaptive (on-line) extension of the (off-line) framework developed in [10]. It relies upon testing of pairs of hypotheses in the case where each hypothesis states that the vector of parameters underlying the dis- tribution of observations belongs to a convex set. The nearly optimal under appropriate conditions test is yielded by a solution to an efficiently solvable convex optimization prob- lem. The proposed methodology can be seen as a computationally friendly reformulation of the classical sequential testing.