{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:IOSSAEYLO6USLLY4UXEXQ47ASC","short_pith_number":"pith:IOSSAEYL","schema_version":"1.0","canonical_sha256":"43a520130b77a925af1ca5c97873e090ad1dd7b062bc9c38f6ba76702d868259","source":{"kind":"arxiv","id":"1409.6276","version":1},"attestation_state":"computed","paper":{"title":"Concentration Inequalities from Likelihood Ratio Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.TH"],"primary_cat":"math.ST","authors_text":"Xinjia Chen","submitted_at":"2014-09-01T17:03:06Z","abstract_excerpt":"We explore the applications of our previously established likelihood-ratio method for deriving concentration inequalities for a wide variety of univariate and multivariate distributions. New concentration inequalities for various distributions are developed without the idea of minimizing moment generating functions."},"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":"1409.6276","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-09-01T17:03:06Z","cross_cats_sorted":["math.PR","stat.TH"],"title_canon_sha256":"da505e89416e2d28e05e0f6e28ded7bb3c6cced72a8aee8c540563660f2aca2d","abstract_canon_sha256":"7dba055b81b5ae16ca12b8d7cdb5c8bf04d4739fef019618336d72dcf076aaa7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:42:13.466704Z","signature_b64":"wq2r9sRWWm6z+TEvXhM8qzL4vDwkyxKa4tgD03xVKHmirsorxEa/wxUsFsA2MKNxYAMk43EaHh10ypdYIMQmAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43a520130b77a925af1ca5c97873e090ad1dd7b062bc9c38f6ba76702d868259","last_reissued_at":"2026-05-18T02:42:13.466332Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:42:13.466332Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Concentration Inequalities from Likelihood Ratio Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","stat.TH"],"primary_cat":"math.ST","authors_text":"Xinjia Chen","submitted_at":"2014-09-01T17:03:06Z","abstract_excerpt":"We explore the applications of our previously established likelihood-ratio method for deriving concentration inequalities for a wide variety of univariate and multivariate distributions. New concentration inequalities for various distributions are developed without the idea of minimizing moment generating functions."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.6276","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":"1409.6276","created_at":"2026-05-18T02:42:13.466389+00:00"},{"alias_kind":"arxiv_version","alias_value":"1409.6276v1","created_at":"2026-05-18T02:42:13.466389+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.6276","created_at":"2026-05-18T02:42:13.466389+00:00"},{"alias_kind":"pith_short_12","alias_value":"IOSSAEYLO6US","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_16","alias_value":"IOSSAEYLO6USLLY4","created_at":"2026-05-18T12:28:33.132498+00:00"},{"alias_kind":"pith_short_8","alias_value":"IOSSAEYL","created_at":"2026-05-18T12:28:33.132498+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/IOSSAEYLO6USLLY4UXEXQ47ASC","json":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC.json","graph_json":"https://pith.science/api/pith-number/IOSSAEYLO6USLLY4UXEXQ47ASC/graph.json","events_json":"https://pith.science/api/pith-number/IOSSAEYLO6USLLY4UXEXQ47ASC/events.json","paper":"https://pith.science/paper/IOSSAEYL"},"agent_actions":{"view_html":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC","download_json":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC.json","view_paper":"https://pith.science/paper/IOSSAEYL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1409.6276&json=true","fetch_graph":"https://pith.science/api/pith-number/IOSSAEYLO6USLLY4UXEXQ47ASC/graph.json","fetch_events":"https://pith.science/api/pith-number/IOSSAEYLO6USLLY4UXEXQ47ASC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC/action/storage_attestation","attest_author":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC/action/author_attestation","sign_citation":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC/action/citation_signature","submit_replication":"https://pith.science/pith/IOSSAEYLO6USLLY4UXEXQ47ASC/action/replication_record"}},"created_at":"2026-05-18T02:42:13.466389+00:00","updated_at":"2026-05-18T02:42:13.466389+00:00"}