{"paper":{"title":"Regression of ranked responses when raw responses are censored","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.AP","authors_text":"Anthony C. Gamst, Ian Abramson, Michael C. Donohue, Robert A. Rissman","submitted_at":"2016-02-24T15:28:33Z","abstract_excerpt":"We discuss semiparametric regression when only the ranks of responses are observed. The model is $Y_i = F (\\mathbf{x}_i'{\\boldsymbol\\beta}_0 + \\varepsilon_i)$, where $Y_i$ is the unobserved response, $F$ is a monotone increasing function, $\\mathbf{x}_i$ is a known $p-$vector of covariates, ${\\boldsymbol\\beta}_0$ is an unknown $p$-vector of interest, and $\\varepsilon_i$ is an error term independent of $\\mathbf{x}_i$. We observe $\\{(\\mathbf{x}_i,R_n(Y_i)) : i = 1,\\ldots ,n\\}$, where $R_n$ is the ordinal rank function. We explore a novel estimator under Gaussian assumptions. We discuss the litera"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.07559","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"}