{"paper":{"title":"Optimal Algorithms and Lower Bounds for Testing Closeness of Structured Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","math.ST","stat.TH"],"primary_cat":"cs.DS","authors_text":"Daniel M. Kane, Ilias Diakonikolas, Vladimir Nikishkin","submitted_at":"2015-08-22T18:49:50Z","abstract_excerpt":"We give a general unified method that can be used for $L_1$ {\\em closeness testing} of a wide range of univariate structured distribution families. More specifically, we design a sample optimal and computationally efficient algorithm for testing the equivalence of two unknown (potentially arbitrary) univariate distributions under the $\\mathcal{A}_k$-distance metric: Given sample access to distributions with density functions $p, q: I \\to \\mathbb{R}$, we want to distinguish between the cases that $p=q$ and $\\|p-q\\|_{\\mathcal{A}_k} \\ge \\epsilon$ with probability at least $2/3$. We show that for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1508.05538","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"}