{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2009:TM6JDJWUOGSMYS3RXN6ZFVLFAL","short_pith_number":"pith:TM6JDJWU","canonical_record":{"source":{"id":"0904.0691","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2009-04-04T06:34:20Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"7608aa0d62fbe1d9177b4c7c1770b12687d5e6c7a8805a6c9c36950759ede81a","abstract_canon_sha256":"77a4082712db08514771e1978293aa5a2fdd87e123afba2cdb4028479a51dc96"},"schema_version":"1.0"},"canonical_sha256":"9b3c91a6d471a4cc4b71bb7d92d56502d4d20778f1bd226b2c034597e056fd63","source":{"kind":"arxiv","id":"0904.0691","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"0904.0691","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"arxiv_version","alias_value":"0904.0691v1","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0904.0691","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"pith_short_12","alias_value":"TM6JDJWUOGSM","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"pith_short_16","alias_value":"TM6JDJWUOGSMYS3R","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"pith_short_8","alias_value":"TM6JDJWU","created_at":"2026-07-04T15:40:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2009:TM6JDJWUOGSMYS3RXN6ZFVLFAL","target":"record","payload":{"canonical_record":{"source":{"id":"0904.0691","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2009-04-04T06:34:20Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"7608aa0d62fbe1d9177b4c7c1770b12687d5e6c7a8805a6c9c36950759ede81a","abstract_canon_sha256":"77a4082712db08514771e1978293aa5a2fdd87e123afba2cdb4028479a51dc96"},"schema_version":"1.0"},"canonical_sha256":"9b3c91a6d471a4cc4b71bb7d92d56502d4d20778f1bd226b2c034597e056fd63","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T15:40:40.165228Z","signature_b64":"DTFGW0hp0o2Rt4sHPljusnYAzs4MHQZ8koGInG0cLdB4SHeoEMHqZxVpNL2OfIC3vp4rkmMIpm0hQkYJG/tVBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b3c91a6d471a4cc4b71bb7d92d56502d4d20778f1bd226b2c034597e056fd63","last_reissued_at":"2026-07-04T15:40:40.164817Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T15:40:40.164817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"0904.0691","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":"2026-07-04T15:40:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zm4jMsNLUqiDM5wkmA1iOT+fZQ1pfU1HdjP/MgIq0v30I3uc6IErtylruA2jnp1LdT1yrlmSQtXgzH3/16udBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T22:48:15.036963Z"},"content_sha256":"6decb1e7cf80f9c30d6f9e8cbad8d49c8724212955a1f3c1e838c6b6112c219e","schema_version":"1.0","event_id":"sha256:6decb1e7cf80f9c30d6f9e8cbad8d49c8724212955a1f3c1e838c6b6112c219e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2009:TM6JDJWUOGSMYS3RXN6ZFVLFAL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Convex Optimization Methods for Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"stat.ME","authors_text":"Ming Yuan, Renato D. C. Monteiro, Zhaosong Lu","submitted_at":"2009-04-04T06:34:20Z","abstract_excerpt":"In this paper, we study convex optimization methods for computing the trace norm regularized least squares estimate in multivariate linear regression. The so-called factor estimation and selection (FES) method, recently proposed by Yuan et al. [22], conducts parameter estimation and factor selection simultaneously and have been shown to enjoy nice properties in both large and finite samples. To compute the estimates, however, can be very challenging in practice because of the high dimensionality and the trace norm constraint. In this paper, we explore a variant of Nesterov's smooth method [20]"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0904.0691","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/0904.0691/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-04T15:40:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H4sS7Ng3BxEhjgCm97QOhNK9Dod3+N0/AOT4Q43ubvYOiW7fVhPFVLL4Cmng76zofh/Hk1XO4BGDPcTBINRnAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T22:48:15.037355Z"},"content_sha256":"673c55229c495e7e63366e442334487c7ffd7661c594d852d23efc6091c15542","schema_version":"1.0","event_id":"sha256:673c55229c495e7e63366e442334487c7ffd7661c594d852d23efc6091c15542"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TM6JDJWUOGSMYS3RXN6ZFVLFAL/bundle.json","state_url":"https://pith.science/pith/TM6JDJWUOGSMYS3RXN6ZFVLFAL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TM6JDJWUOGSMYS3RXN6ZFVLFAL/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":"2026-07-04T22:48:15Z","links":{"resolver":"https://pith.science/pith/TM6JDJWUOGSMYS3RXN6ZFVLFAL","bundle":"https://pith.science/pith/TM6JDJWUOGSMYS3RXN6ZFVLFAL/bundle.json","state":"https://pith.science/pith/TM6JDJWUOGSMYS3RXN6ZFVLFAL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TM6JDJWUOGSMYS3RXN6ZFVLFAL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2009:TM6JDJWUOGSMYS3RXN6ZFVLFAL","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":"77a4082712db08514771e1978293aa5a2fdd87e123afba2cdb4028479a51dc96","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2009-04-04T06:34:20Z","title_canon_sha256":"7608aa0d62fbe1d9177b4c7c1770b12687d5e6c7a8805a6c9c36950759ede81a"},"schema_version":"1.0","source":{"id":"0904.0691","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"0904.0691","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"arxiv_version","alias_value":"0904.0691v1","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0904.0691","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"pith_short_12","alias_value":"TM6JDJWUOGSM","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"pith_short_16","alias_value":"TM6JDJWUOGSMYS3R","created_at":"2026-07-04T15:40:40Z"},{"alias_kind":"pith_short_8","alias_value":"TM6JDJWU","created_at":"2026-07-04T15:40:40Z"}],"graph_snapshots":[{"event_id":"sha256:673c55229c495e7e63366e442334487c7ffd7661c594d852d23efc6091c15542","target":"graph","created_at":"2026-07-04T15:40:40Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/0904.0691/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we study convex optimization methods for computing the trace norm regularized least squares estimate in multivariate linear regression. The so-called factor estimation and selection (FES) method, recently proposed by Yuan et al. [22], conducts parameter estimation and factor selection simultaneously and have been shown to enjoy nice properties in both large and finite samples. To compute the estimates, however, can be very challenging in practice because of the high dimensionality and the trace norm constraint. In this paper, we explore a variant of Nesterov's smooth method [20]","authors_text":"Ming Yuan, Renato D. C. Monteiro, Zhaosong Lu","cross_cats":["stat.CO"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2009-04-04T06:34:20Z","title":"Convex Optimization Methods for Dimension Reduction and Coefficient Estimation in Multivariate Linear Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0904.0691","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":"sha256:6decb1e7cf80f9c30d6f9e8cbad8d49c8724212955a1f3c1e838c6b6112c219e","target":"record","created_at":"2026-07-04T15:40:40Z","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":"77a4082712db08514771e1978293aa5a2fdd87e123afba2cdb4028479a51dc96","cross_cats_sorted":["stat.CO"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2009-04-04T06:34:20Z","title_canon_sha256":"7608aa0d62fbe1d9177b4c7c1770b12687d5e6c7a8805a6c9c36950759ede81a"},"schema_version":"1.0","source":{"id":"0904.0691","kind":"arxiv","version":1}},"canonical_sha256":"9b3c91a6d471a4cc4b71bb7d92d56502d4d20778f1bd226b2c034597e056fd63","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9b3c91a6d471a4cc4b71bb7d92d56502d4d20778f1bd226b2c034597e056fd63","first_computed_at":"2026-07-04T15:40:40.164817Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T15:40:40.164817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DTFGW0hp0o2Rt4sHPljusnYAzs4MHQZ8koGInG0cLdB4SHeoEMHqZxVpNL2OfIC3vp4rkmMIpm0hQkYJG/tVBw==","signature_status":"signed_v1","signed_at":"2026-07-04T15:40:40.165228Z","signed_message":"canonical_sha256_bytes"},"source_id":"0904.0691","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6decb1e7cf80f9c30d6f9e8cbad8d49c8724212955a1f3c1e838c6b6112c219e","sha256:673c55229c495e7e63366e442334487c7ffd7661c594d852d23efc6091c15542"],"state_sha256":"2e2b282531b949aac81d38bd71745f440bf54925eb2bdf5425a15e9a1d515874"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mLCJLS1YuVLOooQXypj1tHFuRcQjipRDJ5qmTm4MPEl507x93g0kDJmMH+qs5zOI4FX56x70TA7Agd6K0/GKDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T22:48:15.039344Z","bundle_sha256":"86e2f4cfd2f2727dd0ffad626b9f784ee951badb84dea4cd1d8fcea081c8fb88"}}