{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:X7HDTHGUYPWUNDIHJO4UKP22EV","short_pith_number":"pith:X7HDTHGU","canonical_record":{"source":{"id":"1304.4890","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-04-17T17:04:10Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"3dc1e1be9bccc6bb4130fc56dc33824d4677ffb2d67235b73d1659f3a32fef4e","abstract_canon_sha256":"b30e2fdf7245d7ff0d6819b9d6b76007779f38a71efbfa1475bfa384009aed52"},"schema_version":"1.0"},"canonical_sha256":"bfce399cd4c3ed468d074bb9453f5a2564cce499e0d724089536ed2ecbffd32b","source":{"kind":"arxiv","id":"1304.4890","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1304.4890","created_at":"2026-05-18T03:27:43Z"},{"alias_kind":"arxiv_version","alias_value":"1304.4890v1","created_at":"2026-05-18T03:27:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1304.4890","created_at":"2026-05-18T03:27:43Z"},{"alias_kind":"pith_short_12","alias_value":"X7HDTHGUYPWU","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_16","alias_value":"X7HDTHGUYPWUNDIH","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_8","alias_value":"X7HDTHGU","created_at":"2026-05-18T12:28:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:X7HDTHGUYPWUNDIHJO4UKP22EV","target":"record","payload":{"canonical_record":{"source":{"id":"1304.4890","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-04-17T17:04:10Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"3dc1e1be9bccc6bb4130fc56dc33824d4677ffb2d67235b73d1659f3a32fef4e","abstract_canon_sha256":"b30e2fdf7245d7ff0d6819b9d6b76007779f38a71efbfa1475bfa384009aed52"},"schema_version":"1.0"},"canonical_sha256":"bfce399cd4c3ed468d074bb9453f5a2564cce499e0d724089536ed2ecbffd32b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:27:43.381211Z","signature_b64":"GOg9M12GJFDlfF/Y+Z/ihRdFGTHAJHqN9pdNrxcFmQHdQxIT5npzTLjj85Zl24YUv864YB8cwIcWyGazNawlBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfce399cd4c3ed468d074bb9453f5a2564cce499e0d724089536ed2ecbffd32b","last_reissued_at":"2026-05-18T03:27:43.380733Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:27:43.380733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1304.4890","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":"2026-05-18T03:27:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nLEucUh1kk3ZU2ojrYh/Yiytqq5Kih+UfFxzX6eNOP5wSSOCEZpUOgKY01+nSiDo4WY0EBEvgXmL/QLsRStvDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:13:31.792878Z"},"content_sha256":"d7762e0ed002fc919ddf2abe9b681511c1e368751c0edf0ec514b0c4dc5c2e52","schema_version":"1.0","event_id":"sha256:d7762e0ed002fc919ddf2abe9b681511c1e368751c0edf0ec514b0c4dc5c2e52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:X7HDTHGUYPWUNDIHJO4UKP22EV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generalized Orthogonal Components Regression for High Dimensional Generalized Linear Models","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Dabao Zhang, Min Zhang, Yanzhu Lin","submitted_at":"2013-04-17T17:04:10Z","abstract_excerpt":"Here we propose an algorithm, named generalized orthogonal components regression (GOCRE), to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a differen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1304.4890","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":"2026-05-18T03:27:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0kwPQm4rEP0040AxPq0MNFNqz9C+rH/GiMysz4LFhPv0m4CfQ9EZOtgEZtEJTQ8opHSAEglgPjztpIJa++e5Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T14:13:31.793567Z"},"content_sha256":"8f845bde050197fd9b08bade3cba1cf5d24061ab80d06665f1e073008206dd61","schema_version":"1.0","event_id":"sha256:8f845bde050197fd9b08bade3cba1cf5d24061ab80d06665f1e073008206dd61"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/X7HDTHGUYPWUNDIHJO4UKP22EV/bundle.json","state_url":"https://pith.science/pith/X7HDTHGUYPWUNDIHJO4UKP22EV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/X7HDTHGUYPWUNDIHJO4UKP22EV/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":"2026-06-08T14:13:31Z","links":{"resolver":"https://pith.science/pith/X7HDTHGUYPWUNDIHJO4UKP22EV","bundle":"https://pith.science/pith/X7HDTHGUYPWUNDIHJO4UKP22EV/bundle.json","state":"https://pith.science/pith/X7HDTHGUYPWUNDIHJO4UKP22EV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/X7HDTHGUYPWUNDIHJO4UKP22EV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:X7HDTHGUYPWUNDIHJO4UKP22EV","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":"b30e2fdf7245d7ff0d6819b9d6b76007779f38a71efbfa1475bfa384009aed52","cross_cats_sorted":["stat.CO"],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-04-17T17:04:10Z","title_canon_sha256":"3dc1e1be9bccc6bb4130fc56dc33824d4677ffb2d67235b73d1659f3a32fef4e"},"schema_version":"1.0","source":{"id":"1304.4890","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1304.4890","created_at":"2026-05-18T03:27:43Z"},{"alias_kind":"arxiv_version","alias_value":"1304.4890v1","created_at":"2026-05-18T03:27:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1304.4890","created_at":"2026-05-18T03:27:43Z"},{"alias_kind":"pith_short_12","alias_value":"X7HDTHGUYPWU","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_16","alias_value":"X7HDTHGUYPWUNDIH","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_8","alias_value":"X7HDTHGU","created_at":"2026-05-18T12:28:06Z"}],"graph_snapshots":[{"event_id":"sha256:8f845bde050197fd9b08bade3cba1cf5d24061ab80d06665f1e073008206dd61","target":"graph","created_at":"2026-05-18T03:27:43Z","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":"Here we propose an algorithm, named generalized orthogonal components regression (GOCRE), to explore the relationship between a categorical outcome and a set of massive variables. A set of orthogonal components are sequentially constructed to account for the variation of the categorical outcome, and together build up a generalized linear model (GLM). This algorithm can be considered as an extension of the partial least squares (PLS) for GLMs, but overcomes several issues of existing extensions based on iteratively reweighted least squares (IRLS). First, existing extensions construct a differen","authors_text":"Dabao Zhang, Min Zhang, Yanzhu Lin","cross_cats":["stat.CO"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-04-17T17:04:10Z","title":"Generalized Orthogonal Components Regression for High Dimensional Generalized Linear Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1304.4890","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:d7762e0ed002fc919ddf2abe9b681511c1e368751c0edf0ec514b0c4dc5c2e52","target":"record","created_at":"2026-05-18T03:27:43Z","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":"b30e2fdf7245d7ff0d6819b9d6b76007779f38a71efbfa1475bfa384009aed52","cross_cats_sorted":["stat.CO"],"license":"http://creativecommons.org/licenses/by-nc-sa/3.0/","primary_cat":"stat.ME","submitted_at":"2013-04-17T17:04:10Z","title_canon_sha256":"3dc1e1be9bccc6bb4130fc56dc33824d4677ffb2d67235b73d1659f3a32fef4e"},"schema_version":"1.0","source":{"id":"1304.4890","kind":"arxiv","version":1}},"canonical_sha256":"bfce399cd4c3ed468d074bb9453f5a2564cce499e0d724089536ed2ecbffd32b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bfce399cd4c3ed468d074bb9453f5a2564cce499e0d724089536ed2ecbffd32b","first_computed_at":"2026-05-18T03:27:43.380733Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:27:43.380733Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GOg9M12GJFDlfF/Y+Z/ihRdFGTHAJHqN9pdNrxcFmQHdQxIT5npzTLjj85Zl24YUv864YB8cwIcWyGazNawlBA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:27:43.381211Z","signed_message":"canonical_sha256_bytes"},"source_id":"1304.4890","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d7762e0ed002fc919ddf2abe9b681511c1e368751c0edf0ec514b0c4dc5c2e52","sha256:8f845bde050197fd9b08bade3cba1cf5d24061ab80d06665f1e073008206dd61"],"state_sha256":"74afe9117d032e8369c1c025598fc4f08caae0154e60ff449db483fce9240eb7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+4CmlEP/h3li+mbEkkh0UvOEeBGm13Z0n/CkHhbPhwpX9/A0u19OY7CQW3zj63Tcoc0fn1SIRqYDdLs0gEDTAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T14:13:31.797267Z","bundle_sha256":"ee5b75e8e8e6b72d5f2fe24e2f82ca9762c9658808ba9da86b4efc15cffc67e8"}}