{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:WKLNTDQBCXSADRNF63TEDPCRRR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"734b62127b400a59940f9f6e9086a79127486ceee35e4790ecbe09b563448e7d","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-10-29T03:58:49Z","title_canon_sha256":"214e5d2260959e7d5afe7ac954eb9ffcaedd2878fc22dbdf1fe34ba87f6e4b18"},"schema_version":"1.0","source":{"id":"1910.13074","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.13074","created_at":"2026-07-05T00:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"1910.13074v1","created_at":"2026-07-05T00:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.13074","created_at":"2026-07-05T00:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"WKLNTDQBCXSA","created_at":"2026-07-05T00:15:26Z"},{"alias_kind":"pith_short_16","alias_value":"WKLNTDQBCXSADRNF","created_at":"2026-07-05T00:15:26Z"},{"alias_kind":"pith_short_8","alias_value":"WKLNTDQB","created_at":"2026-07-05T00:15:26Z"}],"graph_snapshots":[{"event_id":"sha256:342b3d1b74a6497ca1f6253b64805d4fc809af8161c6bb3d900b42f2db5b0ab6","target":"graph","created_at":"2026-07-05T00:15:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1910.13074/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider testing the equality of two high-dimensional covariance matrices by carrying out a multi-level thresholding procedure, which is designed to detect sparse and faint differences between the covariances. A novel U-statistic composition is developed to establish the asymptotic distribution of the thresholding statistics in conjunction with the matrix blocking and the coupling techniques. We propose a multi-thresholding test that is shown to be powerful in detecting sparse and weak differences between two covariance matrices. The test is shown to have attractive detection boundary and t","authors_text":"Bin Guo, Song Xi Chen, Yumou Qiu","cross_cats":["stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-10-29T03:58:49Z","title":"Multi-level Thresholding Test for High Dimensional Covariance Matrices"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.13074","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9db13801235eaa42ef5408273042b615a722f0e79129356c7dee6c51e5793ac7","target":"record","created_at":"2026-07-05T00:15:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"734b62127b400a59940f9f6e9086a79127486ceee35e4790ecbe09b563448e7d","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-10-29T03:58:49Z","title_canon_sha256":"214e5d2260959e7d5afe7ac954eb9ffcaedd2878fc22dbdf1fe34ba87f6e4b18"},"schema_version":"1.0","source":{"id":"1910.13074","kind":"arxiv","version":1}},"canonical_sha256":"b296d98e0115e401c5a5f6e641bc518c49a1720d47e26bade1b6c28cd3908d64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b296d98e0115e401c5a5f6e641bc518c49a1720d47e26bade1b6c28cd3908d64","first_computed_at":"2026-07-05T00:15:26.731063Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:15:26.731063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0fcvBc1zFhKAau3wIgA5+Q54L+icnffMfSyDURq/HwU3rI5WCU8LD/3eSSZl2o5/PjNfXh1sgdn/3ejart2gDw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:15:26.731497Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.13074","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9db13801235eaa42ef5408273042b615a722f0e79129356c7dee6c51e5793ac7","sha256:342b3d1b74a6497ca1f6253b64805d4fc809af8161c6bb3d900b42f2db5b0ab6"],"state_sha256":"58970c33424a04199929a5a665f372567adbc24e2ee802370888d62e2e5ebd51"}