{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:BNWVTOLHP2BOMKQSVWUIUIDQPY","short_pith_number":"pith:BNWVTOLH","canonical_record":{"source":{"id":"1307.3227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-07-11T19:41:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c12583ed6dd40391700a3908e82ba71bf4b3d99d31c279b196f3390ad82e1ab7","abstract_canon_sha256":"ee3f2189a19f91a4d50fb27d5a8017b0cb78b189f6c38b304f8fe04001c25e54"},"schema_version":"1.0"},"canonical_sha256":"0b6d59b9677e82e62a12ada88a20707e326ffea494f01a08456d7dd1504870e9","source":{"kind":"arxiv","id":"1307.3227","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1307.3227","created_at":"2026-05-18T03:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"1307.3227v1","created_at":"2026-05-18T03:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1307.3227","created_at":"2026-05-18T03:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"BNWVTOLHP2BO","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"BNWVTOLHP2BOMKQS","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"BNWVTOLH","created_at":"2026-05-18T12:27:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:BNWVTOLHP2BOMKQSVWUIUIDQPY","target":"record","payload":{"canonical_record":{"source":{"id":"1307.3227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-07-11T19:41:00Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c12583ed6dd40391700a3908e82ba71bf4b3d99d31c279b196f3390ad82e1ab7","abstract_canon_sha256":"ee3f2189a19f91a4d50fb27d5a8017b0cb78b189f6c38b304f8fe04001c25e54"},"schema_version":"1.0"},"canonical_sha256":"0b6d59b9677e82e62a12ada88a20707e326ffea494f01a08456d7dd1504870e9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:18:40.767430Z","signature_b64":"ttQj4+N/GADLAlWzcLvkoEQ6oFc3LJ/Huh80lcwW1jdhBXx39rrq8RjbWUNL/n57uGHiknZrx7yarT7wcXeJBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b6d59b9677e82e62a12ada88a20707e326ffea494f01a08456d7dd1504870e9","last_reissued_at":"2026-05-18T03:18:40.766675Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:18:40.766675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1307.3227","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":"2026-05-18T03:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JUie6Lq44d2DtxeHTppez6jEqmhLgvcmYQey+uXD5IB93fzRGLO2ESFlKuDDPc7ipbckjmevL/QCgzo9GbXMAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T19:57:45.397173Z"},"content_sha256":"e2b4f790fd528e48fc2226073f9e140efdf5280d2781cc2ae26c5a7132064211","schema_version":"1.0","event_id":"sha256:e2b4f790fd528e48fc2226073f9e140efdf5280d2781cc2ae26c5a7132064211"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:BNWVTOLHP2BOMKQSVWUIUIDQPY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Minimum Distance Estimation for Robust High-Dimensional Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Aur\\'elie C. Lozano, Nicolai Meinshausen","submitted_at":"2013-07-11T19:41:00Z","abstract_excerpt":"We propose a minimum distance estimation method for robust regression in sparse high-dimensional settings. The traditional likelihood-based estimators lack resilience against outliers, a critical issue when dealing with high-dimensional noisy data. Our method, Minimum Distance Lasso (MD-Lasso), combines minimum distance functionals, customarily used in nonparametric estimation for their robustness, with l1-regularization for high-dimensional regression. The geometry of MD-Lasso is key to its consistency and robustness. The estimator is governed by a scaling parameter that caps the influence of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1307.3227","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":"2026-05-18T03:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k3WWkKt0BsQIblp0+cCRVv24VWm6jubD6Mhdlf38VrlK1pZrm3/JMr/500dniVvoSyuBdnEq2nrFaH4OYQX/DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T19:57:45.397867Z"},"content_sha256":"5db30e4dbab368f19e8e7797b143801caadfbe1b87d61d33bd4b368b0b2c9d53","schema_version":"1.0","event_id":"sha256:5db30e4dbab368f19e8e7797b143801caadfbe1b87d61d33bd4b368b0b2c9d53"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BNWVTOLHP2BOMKQSVWUIUIDQPY/bundle.json","state_url":"https://pith.science/pith/BNWVTOLHP2BOMKQSVWUIUIDQPY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BNWVTOLHP2BOMKQSVWUIUIDQPY/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":"2026-05-26T19:57:45Z","links":{"resolver":"https://pith.science/pith/BNWVTOLHP2BOMKQSVWUIUIDQPY","bundle":"https://pith.science/pith/BNWVTOLHP2BOMKQSVWUIUIDQPY/bundle.json","state":"https://pith.science/pith/BNWVTOLHP2BOMKQSVWUIUIDQPY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BNWVTOLHP2BOMKQSVWUIUIDQPY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:BNWVTOLHP2BOMKQSVWUIUIDQPY","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":"ee3f2189a19f91a4d50fb27d5a8017b0cb78b189f6c38b304f8fe04001c25e54","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-07-11T19:41:00Z","title_canon_sha256":"c12583ed6dd40391700a3908e82ba71bf4b3d99d31c279b196f3390ad82e1ab7"},"schema_version":"1.0","source":{"id":"1307.3227","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1307.3227","created_at":"2026-05-18T03:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"1307.3227v1","created_at":"2026-05-18T03:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1307.3227","created_at":"2026-05-18T03:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"BNWVTOLHP2BO","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"BNWVTOLHP2BOMKQS","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"BNWVTOLH","created_at":"2026-05-18T12:27:40Z"}],"graph_snapshots":[{"event_id":"sha256:5db30e4dbab368f19e8e7797b143801caadfbe1b87d61d33bd4b368b0b2c9d53","target":"graph","created_at":"2026-05-18T03:18:40Z","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 minimum distance estimation method for robust regression in sparse high-dimensional settings. The traditional likelihood-based estimators lack resilience against outliers, a critical issue when dealing with high-dimensional noisy data. Our method, Minimum Distance Lasso (MD-Lasso), combines minimum distance functionals, customarily used in nonparametric estimation for their robustness, with l1-regularization for high-dimensional regression. The geometry of MD-Lasso is key to its consistency and robustness. The estimator is governed by a scaling parameter that caps the influence of","authors_text":"Aur\\'elie C. Lozano, Nicolai Meinshausen","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-07-11T19:41:00Z","title":"Minimum Distance Estimation for Robust High-Dimensional Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1307.3227","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:e2b4f790fd528e48fc2226073f9e140efdf5280d2781cc2ae26c5a7132064211","target":"record","created_at":"2026-05-18T03:18:40Z","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":"ee3f2189a19f91a4d50fb27d5a8017b0cb78b189f6c38b304f8fe04001c25e54","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-07-11T19:41:00Z","title_canon_sha256":"c12583ed6dd40391700a3908e82ba71bf4b3d99d31c279b196f3390ad82e1ab7"},"schema_version":"1.0","source":{"id":"1307.3227","kind":"arxiv","version":1}},"canonical_sha256":"0b6d59b9677e82e62a12ada88a20707e326ffea494f01a08456d7dd1504870e9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b6d59b9677e82e62a12ada88a20707e326ffea494f01a08456d7dd1504870e9","first_computed_at":"2026-05-18T03:18:40.766675Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:18:40.766675Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ttQj4+N/GADLAlWzcLvkoEQ6oFc3LJ/Huh80lcwW1jdhBXx39rrq8RjbWUNL/n57uGHiknZrx7yarT7wcXeJBA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:18:40.767430Z","signed_message":"canonical_sha256_bytes"},"source_id":"1307.3227","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2b4f790fd528e48fc2226073f9e140efdf5280d2781cc2ae26c5a7132064211","sha256:5db30e4dbab368f19e8e7797b143801caadfbe1b87d61d33bd4b368b0b2c9d53"],"state_sha256":"8393453ddfb9397b9858fa12e8af6ddd4fdf7f4a92a3c549212cc369f4caae86"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0XyncR3QPVT39490tZOj8JrRNXVVXOVwT5LSn9eazcVACEYh4hoY7LuNXRkLYm4PJEMIwAaYsnVGA/U+wx9CBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T19:57:45.401524Z","bundle_sha256":"5f5f811f65ccc6960de2ce353030df00ffffbdfde769b92fb37ac4af9d7c00df"}}