{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:HW3YMUN6L5F5QNXAGF6UKMFJSC","short_pith_number":"pith:HW3YMUN6","canonical_record":{"source":{"id":"1611.01370","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-11-04T13:45:48Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"79690b1bb9e36dd70b74f95f7211616d1aa9b95fa19c3c865757cdf79500b85a","abstract_canon_sha256":"b23145b55c28046c8864d725d9c6e4b45338f4fe4354ac1fd843ac9ff7b1ce82"},"schema_version":"1.0"},"canonical_sha256":"3db78651be5f4bd836e0317d4530a990857964be9e9d673dd291d6fa9cb9f7f6","source":{"kind":"arxiv","id":"1611.01370","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01370","created_at":"2026-05-18T01:00:11Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01370v1","created_at":"2026-05-18T01:00:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01370","created_at":"2026-05-18T01:00:11Z"},{"alias_kind":"pith_short_12","alias_value":"HW3YMUN6L5F5","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"HW3YMUN6L5F5QNXA","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"HW3YMUN6","created_at":"2026-05-18T12:30:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:HW3YMUN6L5F5QNXAGF6UKMFJSC","target":"record","payload":{"canonical_record":{"source":{"id":"1611.01370","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-11-04T13:45:48Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"79690b1bb9e36dd70b74f95f7211616d1aa9b95fa19c3c865757cdf79500b85a","abstract_canon_sha256":"b23145b55c28046c8864d725d9c6e4b45338f4fe4354ac1fd843ac9ff7b1ce82"},"schema_version":"1.0"},"canonical_sha256":"3db78651be5f4bd836e0317d4530a990857964be9e9d673dd291d6fa9cb9f7f6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:11.213055Z","signature_b64":"FkWpnC9LiJYJocBprAsa2vRuh8ktf8HBmO8Uh+JXZfBPz0Z/TAFXxEwfT1lIWkD/jhWDYIinS5VJEeB1xQjODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3db78651be5f4bd836e0317d4530a990857964be9e9d673dd291d6fa9cb9f7f6","last_reissued_at":"2026-05-18T01:00:11.212395Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:11.212395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1611.01370","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:00:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"G9WliVViBsK90rfw7s9E1QR4yrSN/sJY1eBigK7J7UmUIwMkfx1+aSHs9TWYZo8MhS27zDI0MGz0B7SyD/pYDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:28:05.360188Z"},"content_sha256":"db5b686620fe0c9577e3c5413987f2b7357dd706e237d0d0a24268d05cf3a534","schema_version":"1.0","event_id":"sha256:db5b686620fe0c9577e3c5413987f2b7357dd706e237d0d0a24268d05cf3a534"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:HW3YMUN6L5F5QNXAGF6UKMFJSC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Partial autocorrelation parameterization for subset autoregression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"A. Ian McLeod, Ying Zhang","submitted_at":"2016-11-04T13:45:48Z","abstract_excerpt":"A new version of the partial autocorrelation plot and a new family of subset autoregressive models are introduced. A comprehensive approach to model identification, estimation and diagnostic checking is developed for these models. These models are better suited to efficient model building of high-order autoregressions with long time series. Several illustrative examples are given."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01370","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:00:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d7E4v52yeXDxrT/effi9c6+irnm2KxVDP4Yas8zrNaJGs6VS/Hy0KfSaFRZVEGjZY5yus92yj/6j2A280lO8DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T21:28:05.360546Z"},"content_sha256":"65f45ef372212dee8705bfa184bdf36032047b0c39ba2b31ce2860cb10615e24","schema_version":"1.0","event_id":"sha256:65f45ef372212dee8705bfa184bdf36032047b0c39ba2b31ce2860cb10615e24"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HW3YMUN6L5F5QNXAGF6UKMFJSC/bundle.json","state_url":"https://pith.science/pith/HW3YMUN6L5F5QNXAGF6UKMFJSC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HW3YMUN6L5F5QNXAGF6UKMFJSC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-01T21:28:05Z","links":{"resolver":"https://pith.science/pith/HW3YMUN6L5F5QNXAGF6UKMFJSC","bundle":"https://pith.science/pith/HW3YMUN6L5F5QNXAGF6UKMFJSC/bundle.json","state":"https://pith.science/pith/HW3YMUN6L5F5QNXAGF6UKMFJSC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HW3YMUN6L5F5QNXAGF6UKMFJSC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:HW3YMUN6L5F5QNXAGF6UKMFJSC","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":"b23145b55c28046c8864d725d9c6e4b45338f4fe4354ac1fd843ac9ff7b1ce82","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-11-04T13:45:48Z","title_canon_sha256":"79690b1bb9e36dd70b74f95f7211616d1aa9b95fa19c3c865757cdf79500b85a"},"schema_version":"1.0","source":{"id":"1611.01370","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1611.01370","created_at":"2026-05-18T01:00:11Z"},{"alias_kind":"arxiv_version","alias_value":"1611.01370v1","created_at":"2026-05-18T01:00:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.01370","created_at":"2026-05-18T01:00:11Z"},{"alias_kind":"pith_short_12","alias_value":"HW3YMUN6L5F5","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_16","alias_value":"HW3YMUN6L5F5QNXA","created_at":"2026-05-18T12:30:22Z"},{"alias_kind":"pith_short_8","alias_value":"HW3YMUN6","created_at":"2026-05-18T12:30:22Z"}],"graph_snapshots":[{"event_id":"sha256:65f45ef372212dee8705bfa184bdf36032047b0c39ba2b31ce2860cb10615e24","target":"graph","created_at":"2026-05-18T01:00:11Z","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"},"paper":{"abstract_excerpt":"A new version of the partial autocorrelation plot and a new family of subset autoregressive models are introduced. A comprehensive approach to model identification, estimation and diagnostic checking is developed for these models. These models are better suited to efficient model building of high-order autoregressions with long time series. Several illustrative examples are given.","authors_text":"A. Ian McLeod, Ying Zhang","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-11-04T13:45:48Z","title":"Partial autocorrelation parameterization for subset autoregression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.01370","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:db5b686620fe0c9577e3c5413987f2b7357dd706e237d0d0a24268d05cf3a534","target":"record","created_at":"2026-05-18T01:00:11Z","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":"b23145b55c28046c8864d725d9c6e4b45338f4fe4354ac1fd843ac9ff7b1ce82","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-11-04T13:45:48Z","title_canon_sha256":"79690b1bb9e36dd70b74f95f7211616d1aa9b95fa19c3c865757cdf79500b85a"},"schema_version":"1.0","source":{"id":"1611.01370","kind":"arxiv","version":1}},"canonical_sha256":"3db78651be5f4bd836e0317d4530a990857964be9e9d673dd291d6fa9cb9f7f6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3db78651be5f4bd836e0317d4530a990857964be9e9d673dd291d6fa9cb9f7f6","first_computed_at":"2026-05-18T01:00:11.212395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:00:11.212395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FkWpnC9LiJYJocBprAsa2vRuh8ktf8HBmO8Uh+JXZfBPz0Z/TAFXxEwfT1lIWkD/jhWDYIinS5VJEeB1xQjODQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:00:11.213055Z","signed_message":"canonical_sha256_bytes"},"source_id":"1611.01370","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db5b686620fe0c9577e3c5413987f2b7357dd706e237d0d0a24268d05cf3a534","sha256:65f45ef372212dee8705bfa184bdf36032047b0c39ba2b31ce2860cb10615e24"],"state_sha256":"d662c822c32f89d4b9987f01b475a81619a8659b27db8f65b35fdc18014a6408"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MTDnLeQpSsIxzdk+wdj3d6nEBvf1zn75+hTmX7NfTqg3UXtLMTs5CM6CYEWWjxBscisAcuylgKdVqr9O0/QlAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T21:28:05.362406Z","bundle_sha256":"dcbde5a3abe2df4bcf4bb2c0a77fc20429b0d498f68b73d78821f2b48a30fc37"}}