{"paper":{"title":"High dimensional errors-in-variables models with dependent measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Mark Rudelson, Shuheng Zhou","submitted_at":"2015-02-09T04:43:58Z","abstract_excerpt":"Suppose that we observe $y \\in \\mathbb{R}^f$ and $X \\in \\mathbb{R}^{f \\times m}$ in the following errors-in-variables model: \\begin{eqnarray*} y & = & X_0 \\beta^* + \\epsilon \\\\ X & = & X_0 + W \\end{eqnarray*} where $X_0$ is a $f \\times m$ design matrix with independent subgaussian row vectors, $\\epsilon \\in \\mathbb{R}^f$ is a noise vector and $W$ is a mean zero $f \\times m$ random noise matrix with independent subgaussian column vectors, independent of $X_0$ and $\\epsilon$. This model is significantly different from those analyzed in the literature in the sense that we allow the measurement er"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.02355","kind":"arxiv","version":2},"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"}