{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RGYX65YFNUHVTY7VQRLOZ2NEDZ","short_pith_number":"pith:RGYX65YF","schema_version":"1.0","canonical_sha256":"89b17f77056d0f59e3f58456ece9a41e607ecb52bd87c6f3c51a241609fcc870","source":{"kind":"arxiv","id":"2607.11304","version":1},"attestation_state":"computed","paper":{"title":"A Correlation-Free Test for High-Dimensional Elliptical Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Bingye Yang, Minghua Deng, Wenrui Wu, Xu Guo","submitted_at":"2026-07-13T09:20:37Z","abstract_excerpt":"Elliptical distributions provide a flexible and widely used extension of multivariate normal distribution. They play a critical role in many statistical procedures when dealing with high-dimensional data. However, goodness-of-fit testing for elliptical distributions remains challenging when the dimension is comparable to or larger than the sample size. In this work, we propose a correlation-free test for high-dimensional elliptical distributions. We establish high-dimensional Gaussian approximation for the test statistic under general correlation structures, allowing the dimension to grow as $"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.11304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-07-13T09:20:37Z","cross_cats_sorted":[],"title_canon_sha256":"2545816eb2b60ad1e05649da09083b036901439e0ae44787fb25ded161f774ea","abstract_canon_sha256":"443a9265d8af446552c6ed69b0cebec0587334dd24fa5293b5075eabccd57ae7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:22:27.698598Z","signature_b64":"IiKQfgxZp7Y8c9Wu8nbCc+CBtpKJfCMP6lb6jr0jCoUCt19CArz1o9eudWJOlVDxYL2+/5iGEySNq8Eh/QszDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89b17f77056d0f59e3f58456ece9a41e607ecb52bd87c6f3c51a241609fcc870","last_reissued_at":"2026-07-14T01:22:27.697698Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:22:27.697698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Correlation-Free Test for High-Dimensional Elliptical Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Bingye Yang, Minghua Deng, Wenrui Wu, Xu Guo","submitted_at":"2026-07-13T09:20:37Z","abstract_excerpt":"Elliptical distributions provide a flexible and widely used extension of multivariate normal distribution. They play a critical role in many statistical procedures when dealing with high-dimensional data. However, goodness-of-fit testing for elliptical distributions remains challenging when the dimension is comparable to or larger than the sample size. In this work, we propose a correlation-free test for high-dimensional elliptical distributions. We establish high-dimensional Gaussian approximation for the test statistic under general correlation structures, allowing the dimension to grow as $"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11304","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.11304/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.11304","created_at":"2026-07-14T01:22:27.698146+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.11304v1","created_at":"2026-07-14T01:22:27.698146+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11304","created_at":"2026-07-14T01:22:27.698146+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGYX65YFNUHV","created_at":"2026-07-14T01:22:27.698146+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGYX65YFNUHVTY7V","created_at":"2026-07-14T01:22:27.698146+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGYX65YF","created_at":"2026-07-14T01:22:27.698146+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ","json":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ.json","graph_json":"https://pith.science/api/pith-number/RGYX65YFNUHVTY7VQRLOZ2NEDZ/graph.json","events_json":"https://pith.science/api/pith-number/RGYX65YFNUHVTY7VQRLOZ2NEDZ/events.json","paper":"https://pith.science/paper/RGYX65YF"},"agent_actions":{"view_html":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ","download_json":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ.json","view_paper":"https://pith.science/paper/RGYX65YF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.11304&json=true","fetch_graph":"https://pith.science/api/pith-number/RGYX65YFNUHVTY7VQRLOZ2NEDZ/graph.json","fetch_events":"https://pith.science/api/pith-number/RGYX65YFNUHVTY7VQRLOZ2NEDZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ/action/storage_attestation","attest_author":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ/action/author_attestation","sign_citation":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ/action/citation_signature","submit_replication":"https://pith.science/pith/RGYX65YFNUHVTY7VQRLOZ2NEDZ/action/replication_record"}},"created_at":"2026-07-14T01:22:27.698146+00:00","updated_at":"2026-07-14T01:22:27.698146+00:00"}