{"paper":{"title":"A fast algorithm for computing distance correlation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DS"],"primary_cat":"stat.CO","authors_text":"Arin Chaudhuri, Wenhao Hu","submitted_at":"2018-10-26T13:57:23Z","abstract_excerpt":"Classical dependence measures such as Pearson correlation, Spearman's $\\rho$, and Kendall's $\\tau$ can detect only monotonic or linear dependence. To overcome these limitations, Szekely et al.(2007) proposed distance covariance as a weighted $L_2$ distance between the joint characteristic function and the product of marginal distributions. The distance covariance is $0$ if and only if two random vectors ${X}$ and ${Y}$ are independent. This measure has the power to detect the presence of a dependence structure when the sample size is large enough. They further showed that the sample distance c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.11332","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"}