{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2010:GZ7RV6WNWXRLDJIRZN4E74YA5O","short_pith_number":"pith:GZ7RV6WN","canonical_record":{"source":{"id":"1011.3725","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-11-16T15:17:28Z","cross_cats_sorted":[],"title_canon_sha256":"119968696a2cd0e1824700a67791fe68f66d2106d5b88f19cc3a16d83cbe8d11","abstract_canon_sha256":"36c8d7ba97b391e7a79939b20bcf4433297e1abfbffbacb473c748d87cf68ab7"},"schema_version":"1.0"},"canonical_sha256":"367f1afacdb5e2b1a511cb784ff300eb913f2366d57491b193a41a7a2cdecd5f","source":{"kind":"arxiv","id":"1011.3725","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1011.3725","created_at":"2026-05-18T04:35:48Z"},{"alias_kind":"arxiv_version","alias_value":"1011.3725v1","created_at":"2026-05-18T04:35:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1011.3725","created_at":"2026-05-18T04:35:48Z"},{"alias_kind":"pith_short_12","alias_value":"GZ7RV6WNWXRL","created_at":"2026-05-18T12:26:07Z"},{"alias_kind":"pith_short_16","alias_value":"GZ7RV6WNWXRLDJIR","created_at":"2026-05-18T12:26:07Z"},{"alias_kind":"pith_short_8","alias_value":"GZ7RV6WN","created_at":"2026-05-18T12:26:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2010:GZ7RV6WNWXRLDJIRZN4E74YA5O","target":"record","payload":{"canonical_record":{"source":{"id":"1011.3725","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-11-16T15:17:28Z","cross_cats_sorted":[],"title_canon_sha256":"119968696a2cd0e1824700a67791fe68f66d2106d5b88f19cc3a16d83cbe8d11","abstract_canon_sha256":"36c8d7ba97b391e7a79939b20bcf4433297e1abfbffbacb473c748d87cf68ab7"},"schema_version":"1.0"},"canonical_sha256":"367f1afacdb5e2b1a511cb784ff300eb913f2366d57491b193a41a7a2cdecd5f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:35:48.210347Z","signature_b64":"gwSRtx82Xj1LRQaghBCk5NlZvYCUP48uHB9vP7fxwC/cc4HDGcjTJ4puy/3Shl5N+3ZtQaQH+tnspPDOhMyyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"367f1afacdb5e2b1a511cb784ff300eb913f2366d57491b193a41a7a2cdecd5f","last_reissued_at":"2026-05-18T04:35:48.209809Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:35:48.209809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1011.3725","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":"2026-05-18T04:35:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DpIBiniizYmrkFIMt12fGdAttYYLCsweIVPvPiNEyrUMOVrlR1KGVjWMEGnXbuhZNEaxLxmT/h2JJQdBTmhkCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:32:15.764049Z"},"content_sha256":"f4ee165c0fde1cb48e28d1c93e006d78d0ab372cf436c04f41ac05cb8bba7660","schema_version":"1.0","event_id":"sha256:f4ee165c0fde1cb48e28d1c93e006d78d0ab372cf436c04f41ac05cb8bba7660"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2010:GZ7RV6WNWXRLDJIRZN4E74YA5O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predictor-dependent shrinkage for linear regression via partial factor modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Carlos Carvalho, P. Richard Hahn, Sayan Mukherjee","submitted_at":"2010-11-16T15:17:28Z","abstract_excerpt":"In prediction problems with more predictors than observations, it can sometimes be helpful to use a joint probability model, $\\pi(Y,X)$, rather than a purely conditional model, $\\pi(Y \\mid X)$, where $Y$ is a scalar response variable and $X$ is a vector of predictors. This approach is motivated by the fact that in many situations the marginal predictor distribution $\\pi(X)$ can provide useful information about the parameter values governing the conditional regression. However, under very mild misspecification, this marginal distribution can also lead conditional inferences astray. Here, we exp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1011.3725","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":"2026-05-18T04:35:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T6e1SvHjWEiDXqnvvgmYIw7JczZhzbuxvkrKwjICEcJ8oB+/YNPeduZZ9HB/V64pnX4Q2jmRk1rRpg0id+TeDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T04:32:15.764717Z"},"content_sha256":"d07502b12d98b8b5916e06ae8b75070f375fc2346edd83d3eb74e33a54ef34e6","schema_version":"1.0","event_id":"sha256:d07502b12d98b8b5916e06ae8b75070f375fc2346edd83d3eb74e33a54ef34e6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GZ7RV6WNWXRLDJIRZN4E74YA5O/bundle.json","state_url":"https://pith.science/pith/GZ7RV6WNWXRLDJIRZN4E74YA5O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GZ7RV6WNWXRLDJIRZN4E74YA5O/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":"2026-05-27T04:32:15Z","links":{"resolver":"https://pith.science/pith/GZ7RV6WNWXRLDJIRZN4E74YA5O","bundle":"https://pith.science/pith/GZ7RV6WNWXRLDJIRZN4E74YA5O/bundle.json","state":"https://pith.science/pith/GZ7RV6WNWXRLDJIRZN4E74YA5O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GZ7RV6WNWXRLDJIRZN4E74YA5O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2010:GZ7RV6WNWXRLDJIRZN4E74YA5O","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":"36c8d7ba97b391e7a79939b20bcf4433297e1abfbffbacb473c748d87cf68ab7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-11-16T15:17:28Z","title_canon_sha256":"119968696a2cd0e1824700a67791fe68f66d2106d5b88f19cc3a16d83cbe8d11"},"schema_version":"1.0","source":{"id":"1011.3725","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1011.3725","created_at":"2026-05-18T04:35:48Z"},{"alias_kind":"arxiv_version","alias_value":"1011.3725v1","created_at":"2026-05-18T04:35:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1011.3725","created_at":"2026-05-18T04:35:48Z"},{"alias_kind":"pith_short_12","alias_value":"GZ7RV6WNWXRL","created_at":"2026-05-18T12:26:07Z"},{"alias_kind":"pith_short_16","alias_value":"GZ7RV6WNWXRLDJIR","created_at":"2026-05-18T12:26:07Z"},{"alias_kind":"pith_short_8","alias_value":"GZ7RV6WN","created_at":"2026-05-18T12:26:07Z"}],"graph_snapshots":[{"event_id":"sha256:d07502b12d98b8b5916e06ae8b75070f375fc2346edd83d3eb74e33a54ef34e6","target":"graph","created_at":"2026-05-18T04:35:48Z","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":"In prediction problems with more predictors than observations, it can sometimes be helpful to use a joint probability model, $\\pi(Y,X)$, rather than a purely conditional model, $\\pi(Y \\mid X)$, where $Y$ is a scalar response variable and $X$ is a vector of predictors. This approach is motivated by the fact that in many situations the marginal predictor distribution $\\pi(X)$ can provide useful information about the parameter values governing the conditional regression. However, under very mild misspecification, this marginal distribution can also lead conditional inferences astray. Here, we exp","authors_text":"Carlos Carvalho, P. Richard Hahn, Sayan Mukherjee","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-11-16T15:17:28Z","title":"Predictor-dependent shrinkage for linear regression via partial factor modeling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1011.3725","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:f4ee165c0fde1cb48e28d1c93e006d78d0ab372cf436c04f41ac05cb8bba7660","target":"record","created_at":"2026-05-18T04:35:48Z","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":"36c8d7ba97b391e7a79939b20bcf4433297e1abfbffbacb473c748d87cf68ab7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2010-11-16T15:17:28Z","title_canon_sha256":"119968696a2cd0e1824700a67791fe68f66d2106d5b88f19cc3a16d83cbe8d11"},"schema_version":"1.0","source":{"id":"1011.3725","kind":"arxiv","version":1}},"canonical_sha256":"367f1afacdb5e2b1a511cb784ff300eb913f2366d57491b193a41a7a2cdecd5f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"367f1afacdb5e2b1a511cb784ff300eb913f2366d57491b193a41a7a2cdecd5f","first_computed_at":"2026-05-18T04:35:48.209809Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:35:48.209809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gwSRtx82Xj1LRQaghBCk5NlZvYCUP48uHB9vP7fxwC/cc4HDGcjTJ4puy/3Shl5N+3ZtQaQH+tnspPDOhMyyDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T04:35:48.210347Z","signed_message":"canonical_sha256_bytes"},"source_id":"1011.3725","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f4ee165c0fde1cb48e28d1c93e006d78d0ab372cf436c04f41ac05cb8bba7660","sha256:d07502b12d98b8b5916e06ae8b75070f375fc2346edd83d3eb74e33a54ef34e6"],"state_sha256":"03b8c97262eee95ac8e0b2d8bbda6d5eb673b68e2a63d099acb19fa766589cf6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zECINwaRY/RGaPbZcjAMJCl5DhDgTh+evEQHrGlDyeubKJbzmcnSF3PDkao2DGc+7eeKeq4LVpXrn0+FHQRSBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T04:32:15.768281Z","bundle_sha256":"ab1ab0bb321fba05f2b84705048e37b45154f66fd918f3ee0fedd5ac1a014a58"}}