{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:SJXMAY2AYFGB33PCBILRQER5RT","short_pith_number":"pith:SJXMAY2A","canonical_record":{"source":{"id":"1212.0634","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-12-04T07:49:48Z","cross_cats_sorted":["math.ST","stat.CO","stat.ML","stat.TH"],"title_canon_sha256":"931af52bbae89938d2fdd5143dadbc246d614cc1b0d92acee38cda873fbea04a","abstract_canon_sha256":"9f6b0cee1cb474f1ed8b245ce50bc874df1e2bba41d5323a334a256cc974c724"},"schema_version":"1.0"},"canonical_sha256":"926ec06340c14c1dede20a1718123d8cc21645aead7832eb271daa3824c4f32d","source":{"kind":"arxiv","id":"1212.0634","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.0634","created_at":"2026-05-18T03:30:27Z"},{"alias_kind":"arxiv_version","alias_value":"1212.0634v2","created_at":"2026-05-18T03:30:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.0634","created_at":"2026-05-18T03:30:27Z"},{"alias_kind":"pith_short_12","alias_value":"SJXMAY2AYFGB","created_at":"2026-05-18T12:27:20Z"},{"alias_kind":"pith_short_16","alias_value":"SJXMAY2AYFGB33PC","created_at":"2026-05-18T12:27:20Z"},{"alias_kind":"pith_short_8","alias_value":"SJXMAY2A","created_at":"2026-05-18T12:27:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:SJXMAY2AYFGB33PCBILRQER5RT","target":"record","payload":{"canonical_record":{"source":{"id":"1212.0634","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-12-04T07:49:48Z","cross_cats_sorted":["math.ST","stat.CO","stat.ML","stat.TH"],"title_canon_sha256":"931af52bbae89938d2fdd5143dadbc246d614cc1b0d92acee38cda873fbea04a","abstract_canon_sha256":"9f6b0cee1cb474f1ed8b245ce50bc874df1e2bba41d5323a334a256cc974c724"},"schema_version":"1.0"},"canonical_sha256":"926ec06340c14c1dede20a1718123d8cc21645aead7832eb271daa3824c4f32d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:30:27.696123Z","signature_b64":"xG9+yHG+B+KtB9FeziJ3ONT2J0DjMtKdbc27o69+22tix6mtKtnbt4+aNUM7wFMA2RIB/NATyYCtOv2RIvZSAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"926ec06340c14c1dede20a1718123d8cc21645aead7832eb271daa3824c4f32d","last_reissued_at":"2026-05-18T03:30:27.694894Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:30:27.694894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1212.0634","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:30:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JjgUAia9BNwR+OQWlLvZtDWGlnYe9jEHUtmLgZIne4oyeGMf1jZemcp2kdJ0fgfdfG6D2XnjqyqtUbDxK6ZpCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T16:10:23.379893Z"},"content_sha256":"d00d5fe02097912ccf56e2c74f3acb113b8aa2b5ea983a71d703b1f50f207f2f","schema_version":"1.0","event_id":"sha256:d00d5fe02097912ccf56e2c74f3acb113b8aa2b5ea983a71d703b1f50f207f2f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:SJXMAY2AYFGB33PCBILRQER5RT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Better subset regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.CO","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Shifeng Xiong","submitted_at":"2012-12-04T07:49:48Z","abstract_excerpt":"To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model fitting) than all that do not in a general asymptotic setting. This indicates that, for screening important variables, we could follow a \"better fitting, better screening\" rule, i.e., pick a \"better\" subset that has better model fitting. To seek such a better subset, we consider"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.0634","kind":"arxiv","version":2},"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":"2026-05-18T03:30:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nGLPB3gU44DZsKkmm4OHaw0jpkkkz0lT7VbcBwgkj98J1dFb313f0yWpPfxmjtN6Iy68zgmLSn6X3PR+9C+5Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T16:10:23.380242Z"},"content_sha256":"0fa3b12f0b79ac3cad43d2f5f8603d55b8d38638723304896b2f9c9e988eef9b","schema_version":"1.0","event_id":"sha256:0fa3b12f0b79ac3cad43d2f5f8603d55b8d38638723304896b2f9c9e988eef9b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJXMAY2AYFGB33PCBILRQER5RT/bundle.json","state_url":"https://pith.science/pith/SJXMAY2AYFGB33PCBILRQER5RT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJXMAY2AYFGB33PCBILRQER5RT/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":"2026-06-24T16:10:23Z","links":{"resolver":"https://pith.science/pith/SJXMAY2AYFGB33PCBILRQER5RT","bundle":"https://pith.science/pith/SJXMAY2AYFGB33PCBILRQER5RT/bundle.json","state":"https://pith.science/pith/SJXMAY2AYFGB33PCBILRQER5RT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJXMAY2AYFGB33PCBILRQER5RT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:SJXMAY2AYFGB33PCBILRQER5RT","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":"9f6b0cee1cb474f1ed8b245ce50bc874df1e2bba41d5323a334a256cc974c724","cross_cats_sorted":["math.ST","stat.CO","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-12-04T07:49:48Z","title_canon_sha256":"931af52bbae89938d2fdd5143dadbc246d614cc1b0d92acee38cda873fbea04a"},"schema_version":"1.0","source":{"id":"1212.0634","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.0634","created_at":"2026-05-18T03:30:27Z"},{"alias_kind":"arxiv_version","alias_value":"1212.0634v2","created_at":"2026-05-18T03:30:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.0634","created_at":"2026-05-18T03:30:27Z"},{"alias_kind":"pith_short_12","alias_value":"SJXMAY2AYFGB","created_at":"2026-05-18T12:27:20Z"},{"alias_kind":"pith_short_16","alias_value":"SJXMAY2AYFGB33PC","created_at":"2026-05-18T12:27:20Z"},{"alias_kind":"pith_short_8","alias_value":"SJXMAY2A","created_at":"2026-05-18T12:27:20Z"}],"graph_snapshots":[{"event_id":"sha256:0fa3b12f0b79ac3cad43d2f5f8603d55b8d38638723304896b2f9c9e988eef9b","target":"graph","created_at":"2026-05-18T03:30:27Z","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":"To find efficient screening methods for high dimensional linear regression models, this paper studies the relationship between model fitting and screening performance. Under a sparsity assumption, we show that a subset that includes the true submodel always yields smaller residual sum of squares (i.e., has better model fitting) than all that do not in a general asymptotic setting. This indicates that, for screening important variables, we could follow a \"better fitting, better screening\" rule, i.e., pick a \"better\" subset that has better model fitting. To seek such a better subset, we consider","authors_text":"Shifeng Xiong","cross_cats":["math.ST","stat.CO","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-12-04T07:49:48Z","title":"Better subset regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.0634","kind":"arxiv","version":2},"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:d00d5fe02097912ccf56e2c74f3acb113b8aa2b5ea983a71d703b1f50f207f2f","target":"record","created_at":"2026-05-18T03:30:27Z","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":"9f6b0cee1cb474f1ed8b245ce50bc874df1e2bba41d5323a334a256cc974c724","cross_cats_sorted":["math.ST","stat.CO","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-12-04T07:49:48Z","title_canon_sha256":"931af52bbae89938d2fdd5143dadbc246d614cc1b0d92acee38cda873fbea04a"},"schema_version":"1.0","source":{"id":"1212.0634","kind":"arxiv","version":2}},"canonical_sha256":"926ec06340c14c1dede20a1718123d8cc21645aead7832eb271daa3824c4f32d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"926ec06340c14c1dede20a1718123d8cc21645aead7832eb271daa3824c4f32d","first_computed_at":"2026-05-18T03:30:27.694894Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:30:27.694894Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xG9+yHG+B+KtB9FeziJ3ONT2J0DjMtKdbc27o69+22tix6mtKtnbt4+aNUM7wFMA2RIB/NATyYCtOv2RIvZSAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:30:27.696123Z","signed_message":"canonical_sha256_bytes"},"source_id":"1212.0634","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d00d5fe02097912ccf56e2c74f3acb113b8aa2b5ea983a71d703b1f50f207f2f","sha256:0fa3b12f0b79ac3cad43d2f5f8603d55b8d38638723304896b2f9c9e988eef9b"],"state_sha256":"c49ff80a68debe432bcb308955833c220d74f789328c9a2b8ac4d4ff84e9d4a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hZhggHhyWDOiybJ68/H905lNFaJR6uJ5S9Q5ngZYUwxOaxIDNnEVF3GnES1nyV1jHXiuDH6LXDLjfKO4RU9XAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T16:10:23.382185Z","bundle_sha256":"ec0792a5b4104a1ace27c47ae0d3b1b9f55238c8872e366709d2bbed6d9289f9"}}