{"paper":{"title":"Another look at Bootstrapping the Student t-statistic","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Masoud Nasari, Miklos Csorgo, Yuliya Martsynyuk","submitted_at":"2012-09-18T20:02:41Z","abstract_excerpt":"Let X, X_1,X_2,... be a sequence of i.i.d. random variables with mean $\\mu=E X$. Let ${v_1^{(n)},...,v_n^{(n)}}_{n=1}^\\infty$ be vectors of non-negative random variables (weights), independent of the data sequence ${X_1,...,X_n}_{n=1}^\\infty$, and put $m_n=\\sumn v_i^{(n)}$. Consider $ X^{*}_1,..., X^{*}_{m_n}$, $m_n\\geq 1$, a bootstrap sample, resulting from re-sampling or stochastically re-weighing a random sample $X_1,...,X_n$, $n\\geq 1$. Put $\\bar{X}_n= \\sumn X_i/n$, the original sample mean, and define $\\bar{X^*}_{m_n}=\\sumn v_i^{(n)} X_i/m_n$, the bootstrap sample mean. Thus, $\\bar{X^*}_{"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.4089","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"}