{"paper":{"title":"Pivotal Quantities with Arbitrary Small Skewness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Masoud M Nasari","submitted_at":"2016-05-19T14:57:52Z","abstract_excerpt":"In this paper we present randomization methods to enhance the accuracy of the central limit theorem (CLT) based inferences about the population mean $\\mu$. We introduce a broad class of randomized versions of the Student $t$-statistic, the classical pivot for $\\mu$, that continue to possess the pivotal property for $\\mu$ and their skewness can be made arbitrarily small, for each fixed sample size $n$. Consequently, these randomized pivots admit CLTs with smaller errors. The randomization framework in this paper also provides an explicit relation between the precision of the CLTs for the random"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.05985","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"}