{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:XXLLPL2POCOBUMXUOAZVJH5UIL","short_pith_number":"pith:XXLLPL2P","schema_version":"1.0","canonical_sha256":"bdd6b7af4f709c1a32f47033549fb442c08a49a2788777ee7138bd5965c54663","source":{"kind":"arxiv","id":"1711.04238","version":1},"attestation_state":"computed","paper":{"title":"Robust Kullback-Leibler Divergence and Universal Hypothesis Testing for Continuous Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Biao Chen, Pengfei Yang","submitted_at":"2017-11-12T05:20:39Z","abstract_excerpt":"Universal hypothesis testing refers to the problem of deciding whether samples come from a nominal distribution or an unknown distribution that is different from the nominal distribution. Hoeffding's test, whose test statistic is equivalent to the empirical Kullback-Leibler divergence (KLD), is known to be asymptotically optimal for distributions defined on finite alphabets. With continuous observations, however, the discontinuity of the KLD in the distribution functions results in significant complications for universal hypothesis testing. This paper introduces a robust version of the classic"},"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":"1711.04238","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2017-11-12T05:20:39Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"8208a0e8e277cc080f059e038616c11d851501a40df16e9749aa71defd542b62","abstract_canon_sha256":"641f214d05d792bc2a1b2678f8cb445bc335daadfaff972f01b7c2d206ff0a66"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:30:39.688754Z","signature_b64":"aAIUFIkZItLA4O7/A46a3TjrSRhSrrhH1+24AaSwohD7HImXboJ6jhttboWfTulPTqy2m/619HgoX+AHVVRxAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdd6b7af4f709c1a32f47033549fb442c08a49a2788777ee7138bd5965c54663","last_reissued_at":"2026-05-18T00:30:39.687948Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:30:39.687948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Kullback-Leibler Divergence and Universal Hypothesis Testing for Continuous Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Biao Chen, Pengfei Yang","submitted_at":"2017-11-12T05:20:39Z","abstract_excerpt":"Universal hypothesis testing refers to the problem of deciding whether samples come from a nominal distribution or an unknown distribution that is different from the nominal distribution. Hoeffding's test, whose test statistic is equivalent to the empirical Kullback-Leibler divergence (KLD), is known to be asymptotically optimal for distributions defined on finite alphabets. With continuous observations, however, the discontinuity of the KLD in the distribution functions results in significant complications for universal hypothesis testing. This paper introduces a robust version of the classic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.04238","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1711.04238","created_at":"2026-05-18T00:30:39.688086+00:00"},{"alias_kind":"arxiv_version","alias_value":"1711.04238v1","created_at":"2026-05-18T00:30:39.688086+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.04238","created_at":"2026-05-18T00:30:39.688086+00:00"},{"alias_kind":"pith_short_12","alias_value":"XXLLPL2POCOB","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_16","alias_value":"XXLLPL2POCOBUMXU","created_at":"2026-05-18T12:31:56.362134+00:00"},{"alias_kind":"pith_short_8","alias_value":"XXLLPL2P","created_at":"2026-05-18T12:31:56.362134+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/XXLLPL2POCOBUMXUOAZVJH5UIL","json":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL.json","graph_json":"https://pith.science/api/pith-number/XXLLPL2POCOBUMXUOAZVJH5UIL/graph.json","events_json":"https://pith.science/api/pith-number/XXLLPL2POCOBUMXUOAZVJH5UIL/events.json","paper":"https://pith.science/paper/XXLLPL2P"},"agent_actions":{"view_html":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL","download_json":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL.json","view_paper":"https://pith.science/paper/XXLLPL2P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1711.04238&json=true","fetch_graph":"https://pith.science/api/pith-number/XXLLPL2POCOBUMXUOAZVJH5UIL/graph.json","fetch_events":"https://pith.science/api/pith-number/XXLLPL2POCOBUMXUOAZVJH5UIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL/action/storage_attestation","attest_author":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL/action/author_attestation","sign_citation":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL/action/citation_signature","submit_replication":"https://pith.science/pith/XXLLPL2POCOBUMXUOAZVJH5UIL/action/replication_record"}},"created_at":"2026-05-18T00:30:39.688086+00:00","updated_at":"2026-05-18T00:30:39.688086+00:00"}