{"paper":{"title":"Optimal Stopping for Interval Estimation in Bernoulli Trials","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"George V. Moustakides, Tony Yaacoub, Yajun Mei","submitted_at":"2017-11-18T18:26:00Z","abstract_excerpt":"We propose an optimal sequential methodology for obtaining confidence intervals for a binomial proportion $\\theta$. Assuming that an i.i.d. random sequence of Benoulli($\\theta$) trials is observed sequentially, we are interested in designing a)~a stopping time $T$ that will decide when is the best time to stop sampling the process, and b)~an optimum estimator $\\hat{\\theta}_{T}$ that will provide the optimum center of the interval estimate of $\\theta$. We follow a semi-Bayesian approach, where we assume that there exists a prior distribution for $\\theta$, and our goal is to minimize the average"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.06912","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}