{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:5AJWSWKQNI7VM3IJZRXSHKLSRT","short_pith_number":"pith:5AJWSWKQ","canonical_record":{"source":{"id":"1303.7092","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-03-28T11:01:46Z","cross_cats_sorted":["q-fin.ST","stat.TH"],"title_canon_sha256":"0e88e28405c99680db9dbf13a7bf192f74d08a838a5dc03bd2ae2e9cfdd900a7","abstract_canon_sha256":"b124db3969fcc3b763c06986bd06a9c6be1092f9dc74c76811ee1c2cfead0a9b"},"schema_version":"1.0"},"canonical_sha256":"e8136959506a3f566d09cc6f23a9728cd4d727a311ef0786fce6461e9f0c9eb7","source":{"kind":"arxiv","id":"1303.7092","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.7092","created_at":"2026-05-18T03:28:04Z"},{"alias_kind":"arxiv_version","alias_value":"1303.7092v2","created_at":"2026-05-18T03:28:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.7092","created_at":"2026-05-18T03:28:04Z"},{"alias_kind":"pith_short_12","alias_value":"5AJWSWKQNI7V","created_at":"2026-05-18T12:27:34Z"},{"alias_kind":"pith_short_16","alias_value":"5AJWSWKQNI7VM3IJ","created_at":"2026-05-18T12:27:34Z"},{"alias_kind":"pith_short_8","alias_value":"5AJWSWKQ","created_at":"2026-05-18T12:27:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:5AJWSWKQNI7VM3IJZRXSHKLSRT","target":"record","payload":{"canonical_record":{"source":{"id":"1303.7092","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-03-28T11:01:46Z","cross_cats_sorted":["q-fin.ST","stat.TH"],"title_canon_sha256":"0e88e28405c99680db9dbf13a7bf192f74d08a838a5dc03bd2ae2e9cfdd900a7","abstract_canon_sha256":"b124db3969fcc3b763c06986bd06a9c6be1092f9dc74c76811ee1c2cfead0a9b"},"schema_version":"1.0"},"canonical_sha256":"e8136959506a3f566d09cc6f23a9728cd4d727a311ef0786fce6461e9f0c9eb7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:28:04.761313Z","signature_b64":"PdCeC+uMlXuWTsgbxl5TfvACiMSr5LyudXZknYCLY1+OOSYx+uIgglcuwygqjfevq/w6bC13LKI/7uP/vdfuBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8136959506a3f566d09cc6f23a9728cd4d727a311ef0786fce6461e9f0c9eb7","last_reissued_at":"2026-05-18T03:28:04.760656Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:28:04.760656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1303.7092","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":"2026-05-18T03:28:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B+JYuU7tA6stF6Q0UWU13d87jsXEMhaSe+GSSel42OsjzHrv7UvnlUMYKBAuaxG0FPqgkDL0A1OjVMDsw/UwDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T12:11:49.482129Z"},"content_sha256":"d669140d751da622229f415a0335112f72f1e27b9c51fc3553906f329bc4a80d","schema_version":"1.0","event_id":"sha256:d669140d751da622229f415a0335112f72f1e27b9c51fc3553906f329bc4a80d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:5AJWSWKQNI7VM3IJZRXSHKLSRT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Pivotal estimation in high-dimensional regression via linear programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.ST","stat.TH"],"primary_cat":"math.ST","authors_text":"Alexandre Tsybakov (CREST, ENSAE), Eric Gautier (CREST","submitted_at":"2013-03-28T11:01:46Z","abstract_excerpt":"We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and does not require the knowledge of the variance of random errors. The method is based on linear programming only, so that its numerical implementation is faster than for previously known techniques using conic programs, and it allows one to deal with higher dimensional models. We provide upper bounds for estimation and prediction errors of the proposed estimator showing that it achieves the same rate as in the more restrictive si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.7092","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":"2026-05-18T03:28:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+qyiKa7eaWoKqELJLmJNlrHBsa4795M1KeX9s4pzrm2D1IS69ccjWuqZnDHPViqRVjmaVhSX3nzAeQGDeT8GAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T12:11:49.482947Z"},"content_sha256":"a729ecfb5f9000aea2e1d2a9a825f06d5fa06939373922fdd08d394de13becd8","schema_version":"1.0","event_id":"sha256:a729ecfb5f9000aea2e1d2a9a825f06d5fa06939373922fdd08d394de13becd8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5AJWSWKQNI7VM3IJZRXSHKLSRT/bundle.json","state_url":"https://pith.science/pith/5AJWSWKQNI7VM3IJZRXSHKLSRT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5AJWSWKQNI7VM3IJZRXSHKLSRT/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":"2026-07-01T12:11:49Z","links":{"resolver":"https://pith.science/pith/5AJWSWKQNI7VM3IJZRXSHKLSRT","bundle":"https://pith.science/pith/5AJWSWKQNI7VM3IJZRXSHKLSRT/bundle.json","state":"https://pith.science/pith/5AJWSWKQNI7VM3IJZRXSHKLSRT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5AJWSWKQNI7VM3IJZRXSHKLSRT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:5AJWSWKQNI7VM3IJZRXSHKLSRT","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":"b124db3969fcc3b763c06986bd06a9c6be1092f9dc74c76811ee1c2cfead0a9b","cross_cats_sorted":["q-fin.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-03-28T11:01:46Z","title_canon_sha256":"0e88e28405c99680db9dbf13a7bf192f74d08a838a5dc03bd2ae2e9cfdd900a7"},"schema_version":"1.0","source":{"id":"1303.7092","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1303.7092","created_at":"2026-05-18T03:28:04Z"},{"alias_kind":"arxiv_version","alias_value":"1303.7092v2","created_at":"2026-05-18T03:28:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.7092","created_at":"2026-05-18T03:28:04Z"},{"alias_kind":"pith_short_12","alias_value":"5AJWSWKQNI7V","created_at":"2026-05-18T12:27:34Z"},{"alias_kind":"pith_short_16","alias_value":"5AJWSWKQNI7VM3IJ","created_at":"2026-05-18T12:27:34Z"},{"alias_kind":"pith_short_8","alias_value":"5AJWSWKQ","created_at":"2026-05-18T12:27:34Z"}],"graph_snapshots":[{"event_id":"sha256:a729ecfb5f9000aea2e1d2a9a825f06d5fa06939373922fdd08d394de13becd8","target":"graph","created_at":"2026-05-18T03:28:04Z","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":"We propose a new method of estimation in high-dimensional linear regression model. It allows for very weak distributional assumptions including heteroscedasticity, and does not require the knowledge of the variance of random errors. The method is based on linear programming only, so that its numerical implementation is faster than for previously known techniques using conic programs, and it allows one to deal with higher dimensional models. We provide upper bounds for estimation and prediction errors of the proposed estimator showing that it achieves the same rate as in the more restrictive si","authors_text":"Alexandre Tsybakov (CREST, ENSAE), Eric Gautier (CREST","cross_cats":["q-fin.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-03-28T11:01:46Z","title":"Pivotal estimation in high-dimensional regression via linear programming"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.7092","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:d669140d751da622229f415a0335112f72f1e27b9c51fc3553906f329bc4a80d","target":"record","created_at":"2026-05-18T03:28:04Z","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":"b124db3969fcc3b763c06986bd06a9c6be1092f9dc74c76811ee1c2cfead0a9b","cross_cats_sorted":["q-fin.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2013-03-28T11:01:46Z","title_canon_sha256":"0e88e28405c99680db9dbf13a7bf192f74d08a838a5dc03bd2ae2e9cfdd900a7"},"schema_version":"1.0","source":{"id":"1303.7092","kind":"arxiv","version":2}},"canonical_sha256":"e8136959506a3f566d09cc6f23a9728cd4d727a311ef0786fce6461e9f0c9eb7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e8136959506a3f566d09cc6f23a9728cd4d727a311ef0786fce6461e9f0c9eb7","first_computed_at":"2026-05-18T03:28:04.760656Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:28:04.760656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PdCeC+uMlXuWTsgbxl5TfvACiMSr5LyudXZknYCLY1+OOSYx+uIgglcuwygqjfevq/w6bC13LKI/7uP/vdfuBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T03:28:04.761313Z","signed_message":"canonical_sha256_bytes"},"source_id":"1303.7092","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d669140d751da622229f415a0335112f72f1e27b9c51fc3553906f329bc4a80d","sha256:a729ecfb5f9000aea2e1d2a9a825f06d5fa06939373922fdd08d394de13becd8"],"state_sha256":"c8b9afd00b08e0f9d85943aaa495ffcf1a313ba3101ac97efe7baaddd746329a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R62uYTlxw3griZ0aV220bEMoy1m1d9ySVdzGkyS/8+wHO6bF0UkmpycJV1M4RZJ1S8oYkkELn27Whw6+9QqhDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T12:11:49.485247Z","bundle_sha256":"dc191f60e84626ccb44e5523cf07738de952ab62d84ebe39f96adfcb19b57429"}}