{"paper":{"title":"Statistical inference and feasibility determination: a nonasymptotic approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Ying Zhu","submitted_at":"2018-08-17T16:05:58Z","abstract_excerpt":"We develop non-asymptotically justified methods for hypothesis testing about the $p-$dimensional coefficients $\\theta^{*}$ in (possibly nonlinear) regression models. Given a function $h:\\,\\mathbb{R}^{p}\\mapsto\\mathbb{R}^{m}$, we consider the null hypothesis $H_{0}:\\,h(\\theta^{*})\\in\\Omega$ against the alternative hypothesis $H_{1}:\\,h(\\theta^{*})\\notin\\Omega$, where $\\Omega$ is a nonempty closed subset of $\\mathbb{R}^{m}$ and $h$ can be nonlinear in $\\theta^{*}$. Our (nonasymptotic) control on the Type I and Type II errors holds for fixed $n$ and does not rely on well-behaved estimation error "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07127","kind":"arxiv","version":4},"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"}