{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:STSN63FXOR6MKVFKQA334JTB3L","short_pith_number":"pith:STSN63FX","canonical_record":{"source":{"id":"1104.4595","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-04-24T00:03:25Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"030e3e24487ecc2dee02d7efdbbb2b435ca359fae6ac3a13987e759ffa8cba45","abstract_canon_sha256":"a94910b979e94d9275b08047cea775615cc6ac13060f43bcb938cca858090bf3"},"schema_version":"1.0"},"canonical_sha256":"94e4df6cb7747cc554aa8037be2661daf86a809762ba81f631c6eb3f4fa39346","source":{"kind":"arxiv","id":"1104.4595","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1104.4595","created_at":"2026-05-18T03:53:05Z"},{"alias_kind":"arxiv_version","alias_value":"1104.4595v2","created_at":"2026-05-18T03:53:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1104.4595","created_at":"2026-05-18T03:53:05Z"},{"alias_kind":"pith_short_12","alias_value":"STSN63FXOR6M","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_16","alias_value":"STSN63FXOR6MKVFK","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_8","alias_value":"STSN63FX","created_at":"2026-05-18T12:26:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:STSN63FXOR6MKVFKQA334JTB3L","target":"record","payload":{"canonical_record":{"source":{"id":"1104.4595","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-04-24T00:03:25Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"030e3e24487ecc2dee02d7efdbbb2b435ca359fae6ac3a13987e759ffa8cba45","abstract_canon_sha256":"a94910b979e94d9275b08047cea775615cc6ac13060f43bcb938cca858090bf3"},"schema_version":"1.0"},"canonical_sha256":"94e4df6cb7747cc554aa8037be2661daf86a809762ba81f631c6eb3f4fa39346","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:53:05.122831Z","signature_b64":"Z8IWiBxxM2eKBjwCv6DwToGagsnI2P6SB4pW2nM6jVlRVC1yI4JLK5Yr5ikUn1oz3J9VwCvuMP6AB0Wd/Os8Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94e4df6cb7747cc554aa8037be2661daf86a809762ba81f631c6eb3f4fa39346","last_reissued_at":"2026-05-18T03:53:05.122048Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:53:05.122048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1104.4595","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":"2026-05-18T03:53:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uaGJk00UC7GzSKAEsuV/VqMh2xzh58VfaDMP4B7SqZl6TN8IWPCxZl1sUzmPUWSKtqEuZ+ID57nnnlrXvh7zBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T06:25:24.409494Z"},"content_sha256":"683341bd334b18a008c39a25c3048807356ce7a09ef8bca6611e5c0a017f8fd4","schema_version":"1.0","event_id":"sha256:683341bd334b18a008c39a25c3048807356ce7a09ef8bca6611e5c0a017f8fd4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:STSN63FXOR6MKVFKQA334JTB3L","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scaled Sparse Linear Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ML","authors_text":"Cun-Hui Zhang, Tingni Sun","submitted_at":"2011-04-24T00:03:25Z","abstract_excerpt":"Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs little beyond the computation of a path or grid of the sparse regression estimator for penalty levels above a proper threshold. For the scaled lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1104.4595","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":"2026-05-18T03:53:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CW2GQlHaE8en9KFfHWlOmUYHbb160Xj9+Vvd3CPxcglynKkagojRI8v/AiBKLoBVs//VUPC3JjdK41ayz9dqAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T06:25:24.409853Z"},"content_sha256":"e6f849f3322b0731e6852a34a36466d99b4e8aa5e59c81cd9b62fa7bdde3d22d","schema_version":"1.0","event_id":"sha256:e6f849f3322b0731e6852a34a36466d99b4e8aa5e59c81cd9b62fa7bdde3d22d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/STSN63FXOR6MKVFKQA334JTB3L/bundle.json","state_url":"https://pith.science/pith/STSN63FXOR6MKVFKQA334JTB3L/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/STSN63FXOR6MKVFKQA334JTB3L/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":"2026-05-28T06:25:24Z","links":{"resolver":"https://pith.science/pith/STSN63FXOR6MKVFKQA334JTB3L","bundle":"https://pith.science/pith/STSN63FXOR6MKVFKQA334JTB3L/bundle.json","state":"https://pith.science/pith/STSN63FXOR6MKVFKQA334JTB3L/state.json","well_known_bundle":"https://pith.science/.well-known/pith/STSN63FXOR6MKVFKQA334JTB3L/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:STSN63FXOR6MKVFKQA334JTB3L","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":"a94910b979e94d9275b08047cea775615cc6ac13060f43bcb938cca858090bf3","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-04-24T00:03:25Z","title_canon_sha256":"030e3e24487ecc2dee02d7efdbbb2b435ca359fae6ac3a13987e759ffa8cba45"},"schema_version":"1.0","source":{"id":"1104.4595","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1104.4595","created_at":"2026-05-18T03:53:05Z"},{"alias_kind":"arxiv_version","alias_value":"1104.4595v2","created_at":"2026-05-18T03:53:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1104.4595","created_at":"2026-05-18T03:53:05Z"},{"alias_kind":"pith_short_12","alias_value":"STSN63FXOR6M","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_16","alias_value":"STSN63FXOR6MKVFK","created_at":"2026-05-18T12:26:41Z"},{"alias_kind":"pith_short_8","alias_value":"STSN63FX","created_at":"2026-05-18T12:26:41Z"}],"graph_snapshots":[{"event_id":"sha256:e6f849f3322b0731e6852a34a36466d99b4e8aa5e59c81cd9b62fa7bdde3d22d","target":"graph","created_at":"2026-05-18T03:53:05Z","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":"Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual square and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs little beyond the computation of a path or grid of the sparse regression estimator for penalty levels above a proper threshold. For the scaled lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regres","authors_text":"Cun-Hui Zhang, Tingni Sun","cross_cats":["math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-04-24T00:03:25Z","title":"Scaled Sparse Linear Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1104.4595","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":"sha256:683341bd334b18a008c39a25c3048807356ce7a09ef8bca6611e5c0a017f8fd4","target":"record","created_at":"2026-05-18T03:53:05Z","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":"a94910b979e94d9275b08047cea775615cc6ac13060f43bcb938cca858090bf3","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2011-04-24T00:03:25Z","title_canon_sha256":"030e3e24487ecc2dee02d7efdbbb2b435ca359fae6ac3a13987e759ffa8cba45"},"schema_version":"1.0","source":{"id":"1104.4595","kind":"arxiv","version":2}},"canonical_sha256":"94e4df6cb7747cc554aa8037be2661daf86a809762ba81f631c6eb3f4fa39346","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94e4df6cb7747cc554aa8037be2661daf86a809762ba81f631c6eb3f4fa39346","first_computed_at":"2026-05-18T03:53:05.122048Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:53:05.122048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z8IWiBxxM2eKBjwCv6DwToGagsnI2P6SB4pW2nM6jVlRVC1yI4JLK5Yr5ikUn1oz3J9VwCvuMP6AB0Wd/Os8Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:53:05.122831Z","signed_message":"canonical_sha256_bytes"},"source_id":"1104.4595","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:683341bd334b18a008c39a25c3048807356ce7a09ef8bca6611e5c0a017f8fd4","sha256:e6f849f3322b0731e6852a34a36466d99b4e8aa5e59c81cd9b62fa7bdde3d22d"],"state_sha256":"a92667ecef98c05d943dd4a15a39ff9400b0c05a0b2e653dee188212ebf0dfbe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QtQS6Ww5Rk8D4j0tNM6RIuPQfR4JcBmdZcu1NXAfkUyZI7ydnClDoSOtb+bzwIVMBQwnvp8oS65L61Hl0ehLCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T06:25:24.411949Z","bundle_sha256":"1c767f7de9546a9b313bf44df3b854a7b70a22c1cdc25734eac331c99db5a91f"}}