{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:W2ZQSBGNPJAISPF44L5BE2QBJI","short_pith_number":"pith:W2ZQSBGN","canonical_record":{"source":{"id":"1201.3528","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"stat.CO","submitted_at":"2012-01-17T15:18:25Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"ee5c7eb7750a0e3744aad994129889b1c7d5da45295ba9b6065f1fb9c807bd40","abstract_canon_sha256":"7cafe748d184d67911e98f3c4d9b99a419dbe65f0a754edc748850f0804e85a7"},"schema_version":"1.0"},"canonical_sha256":"b6b30904cd7a40893cbce2fa126a014a36acd453745a5b0aea1d14e76ca26bff","source":{"kind":"arxiv","id":"1201.3528","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.3528","created_at":"2026-05-18T04:04:29Z"},{"alias_kind":"arxiv_version","alias_value":"1201.3528v1","created_at":"2026-05-18T04:04:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.3528","created_at":"2026-05-18T04:04:29Z"},{"alias_kind":"pith_short_12","alias_value":"W2ZQSBGNPJAI","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_16","alias_value":"W2ZQSBGNPJAISPF4","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_8","alias_value":"W2ZQSBGN","created_at":"2026-05-18T12:27:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:W2ZQSBGNPJAISPF44L5BE2QBJI","target":"record","payload":{"canonical_record":{"source":{"id":"1201.3528","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"stat.CO","submitted_at":"2012-01-17T15:18:25Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"ee5c7eb7750a0e3744aad994129889b1c7d5da45295ba9b6065f1fb9c807bd40","abstract_canon_sha256":"7cafe748d184d67911e98f3c4d9b99a419dbe65f0a754edc748850f0804e85a7"},"schema_version":"1.0"},"canonical_sha256":"b6b30904cd7a40893cbce2fa126a014a36acd453745a5b0aea1d14e76ca26bff","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:04:29.037646Z","signature_b64":"mo3TFqkFIedEjY+NP+4npjcPDLpf8ehKdfTGMoDDL4LKOtLYU3lay9bGP+Mx20MRVUM29HRTevmlTxjxlWwmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6b30904cd7a40893cbce2fa126a014a36acd453745a5b0aea1d14e76ca26bff","last_reissued_at":"2026-05-18T04:04:29.037006Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:04:29.037006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1201.3528","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":"2026-05-18T04:04:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wKdNBPJ9HsaU8PpurBkk0E9Uib/9kI7XUbBTLug62QAk1iWNwdPqgWuq8sWQ7uXDVGWGDDMe1P/f1KysoedNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T14:51:46.584227Z"},"content_sha256":"a218c29dc31895afac0d8d74e4cd8b5c098dd568b1079878f40c777d26d2970b","schema_version":"1.0","event_id":"sha256:a218c29dc31895afac0d8d74e4cd8b5c098dd568b1079878f40c777d26d2970b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:W2ZQSBGNPJAISPF44L5BE2QBJI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Path Following and Empirical Bayes Model Selection for Sparse Regression","license":"http://creativecommons.org/licenses/by/3.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Artin Armagan, David B. Dunson, Hua Zhou","submitted_at":"2012-01-17T15:18:25Z","abstract_excerpt":"In recent years, a rich variety of regularization procedures have been proposed for high dimensional regression problems. However, tuning parameter choice and computational efficiency in ultra-high dimensional problems remain vexing issues. The routine use of $\\ell_1$ regularization is largely attributable to the computational efficiency of the LARS algorithm, but similar efficiency for better behaved penalties has remained elusive. In this article, we propose a highly efficient path following procedure for combination of any convex loss function and a broad class of penalties. From a Bayesian"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.3528","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":"2026-05-18T04:04:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ftPdD/tDF163p1TPwS63ZrKqX6AgQxL1ikiTCtdp8p6hl0nWl22+PtthXSZTCAt4huD9kGQfwYnwWSQNGAltCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T14:51:46.584582Z"},"content_sha256":"6763284156c7b939b7cc44d6b7c0610dd846251e0c352a869212c4a18791771e","schema_version":"1.0","event_id":"sha256:6763284156c7b939b7cc44d6b7c0610dd846251e0c352a869212c4a18791771e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W2ZQSBGNPJAISPF44L5BE2QBJI/bundle.json","state_url":"https://pith.science/pith/W2ZQSBGNPJAISPF44L5BE2QBJI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W2ZQSBGNPJAISPF44L5BE2QBJI/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":"2026-06-26T14:51:46Z","links":{"resolver":"https://pith.science/pith/W2ZQSBGNPJAISPF44L5BE2QBJI","bundle":"https://pith.science/pith/W2ZQSBGNPJAISPF44L5BE2QBJI/bundle.json","state":"https://pith.science/pith/W2ZQSBGNPJAISPF44L5BE2QBJI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W2ZQSBGNPJAISPF44L5BE2QBJI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:W2ZQSBGNPJAISPF44L5BE2QBJI","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":"7cafe748d184d67911e98f3c4d9b99a419dbe65f0a754edc748850f0804e85a7","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"stat.CO","submitted_at":"2012-01-17T15:18:25Z","title_canon_sha256":"ee5c7eb7750a0e3744aad994129889b1c7d5da45295ba9b6065f1fb9c807bd40"},"schema_version":"1.0","source":{"id":"1201.3528","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.3528","created_at":"2026-05-18T04:04:29Z"},{"alias_kind":"arxiv_version","alias_value":"1201.3528v1","created_at":"2026-05-18T04:04:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.3528","created_at":"2026-05-18T04:04:29Z"},{"alias_kind":"pith_short_12","alias_value":"W2ZQSBGNPJAI","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_16","alias_value":"W2ZQSBGNPJAISPF4","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_8","alias_value":"W2ZQSBGN","created_at":"2026-05-18T12:27:25Z"}],"graph_snapshots":[{"event_id":"sha256:6763284156c7b939b7cc44d6b7c0610dd846251e0c352a869212c4a18791771e","target":"graph","created_at":"2026-05-18T04:04:29Z","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 recent years, a rich variety of regularization procedures have been proposed for high dimensional regression problems. However, tuning parameter choice and computational efficiency in ultra-high dimensional problems remain vexing issues. The routine use of $\\ell_1$ regularization is largely attributable to the computational efficiency of the LARS algorithm, but similar efficiency for better behaved penalties has remained elusive. In this article, we propose a highly efficient path following procedure for combination of any convex loss function and a broad class of penalties. From a Bayesian","authors_text":"Artin Armagan, David B. Dunson, Hua Zhou","cross_cats":["stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"stat.CO","submitted_at":"2012-01-17T15:18:25Z","title":"Path Following and Empirical Bayes Model Selection for Sparse Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.3528","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:a218c29dc31895afac0d8d74e4cd8b5c098dd568b1079878f40c777d26d2970b","target":"record","created_at":"2026-05-18T04:04:29Z","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":"7cafe748d184d67911e98f3c4d9b99a419dbe65f0a754edc748850f0804e85a7","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by/3.0/","primary_cat":"stat.CO","submitted_at":"2012-01-17T15:18:25Z","title_canon_sha256":"ee5c7eb7750a0e3744aad994129889b1c7d5da45295ba9b6065f1fb9c807bd40"},"schema_version":"1.0","source":{"id":"1201.3528","kind":"arxiv","version":1}},"canonical_sha256":"b6b30904cd7a40893cbce2fa126a014a36acd453745a5b0aea1d14e76ca26bff","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b6b30904cd7a40893cbce2fa126a014a36acd453745a5b0aea1d14e76ca26bff","first_computed_at":"2026-05-18T04:04:29.037006Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:04:29.037006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mo3TFqkFIedEjY+NP+4npjcPDLpf8ehKdfTGMoDDL4LKOtLYU3lay9bGP+Mx20MRVUM29HRTevmlTxjxlWwmCw==","signature_status":"signed_v1","signed_at":"2026-05-18T04:04:29.037646Z","signed_message":"canonical_sha256_bytes"},"source_id":"1201.3528","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a218c29dc31895afac0d8d74e4cd8b5c098dd568b1079878f40c777d26d2970b","sha256:6763284156c7b939b7cc44d6b7c0610dd846251e0c352a869212c4a18791771e"],"state_sha256":"e978716c75843d2e508ed76847e2dcfc8a2f9d9e12092cf6b6a2c97d41a2d7a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g5G6MBCW5/d4y5ag4E2laBl6+il18+kHK0AYTtV8i4xhB5nywdNYneVaeyO2GvxxrU9ajd4MCHCgzHwdazgqCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T14:51:46.590151Z","bundle_sha256":"a95bf52dfda639e63ae1d73e1e36f817b560cfff43da0b7b44e79f5ebc18513f"}}