{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:33WB6VZEONNNZH757PBZ33QY6A","short_pith_number":"pith:33WB6VZE","schema_version":"1.0","canonical_sha256":"deec1f5724735adc9ffdfbc39dee18f020e3d51082e01603955a873d39163e49","source":{"kind":"arxiv","id":"1604.07451","version":3},"attestation_state":"computed","paper":{"title":"Learning Local Dependence In Ordered Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Guo Yu, Jacob Bien","submitted_at":"2016-04-25T21:20:51Z","abstract_excerpt":"In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covariance matrix. Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors. Our proposed method "},"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":"1604.07451","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-04-25T21:20:51Z","cross_cats_sorted":["stat.CO","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"3a1815269c90f0deaa7864e0b016162d9e5491e9748f321166798a2ffc00ea25","abstract_canon_sha256":"b918c59a94649d432630411a99dbdefd449df36390658cedced5f8c9e437e3f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:32.589496Z","signature_b64":"RlCKx1mAYdWjIsFjPKpPCD54J7Rtn6pj87fM8oYDKbEOdbtclqK/jW/qy1FXNX+z8m7ppb7kcdJq3c9/mMraAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"deec1f5724735adc9ffdfbc39dee18f020e3d51082e01603955a873d39163e49","last_reissued_at":"2026-05-18T00:28:32.588544Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:32.588544Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Local Dependence In Ordered Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Guo Yu, Jacob Bien","submitted_at":"2016-04-25T21:20:51Z","abstract_excerpt":"In many applications, data come with a natural ordering. This ordering can often induce local dependence among nearby variables. However, in complex data, the width of this dependence may vary, making simple assumptions such as a constant neighborhood size unrealistic. We propose a framework for learning this local dependence based on estimating the inverse of the Cholesky factor of the covariance matrix. Penalized maximum likelihood estimation of this matrix yields a simple regression interpretation for local dependence in which variables are predicted by their neighbors. Our proposed method "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.07451","kind":"arxiv","version":3},"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":"1604.07451","created_at":"2026-05-18T00:28:32.588733+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.07451v3","created_at":"2026-05-18T00:28:32.588733+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.07451","created_at":"2026-05-18T00:28:32.588733+00:00"},{"alias_kind":"pith_short_12","alias_value":"33WB6VZEONNN","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"33WB6VZEONNNZH75","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"33WB6VZE","created_at":"2026-05-18T12:29:55.572404+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/33WB6VZEONNNZH757PBZ33QY6A","json":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A.json","graph_json":"https://pith.science/api/pith-number/33WB6VZEONNNZH757PBZ33QY6A/graph.json","events_json":"https://pith.science/api/pith-number/33WB6VZEONNNZH757PBZ33QY6A/events.json","paper":"https://pith.science/paper/33WB6VZE"},"agent_actions":{"view_html":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A","download_json":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A.json","view_paper":"https://pith.science/paper/33WB6VZE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.07451&json=true","fetch_graph":"https://pith.science/api/pith-number/33WB6VZEONNNZH757PBZ33QY6A/graph.json","fetch_events":"https://pith.science/api/pith-number/33WB6VZEONNNZH757PBZ33QY6A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/action/storage_attestation","attest_author":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/action/author_attestation","sign_citation":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/action/citation_signature","submit_replication":"https://pith.science/pith/33WB6VZEONNNZH757PBZ33QY6A/action/replication_record"}},"created_at":"2026-05-18T00:28:32.588733+00:00","updated_at":"2026-05-18T00:28:32.588733+00:00"}