{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:4HPTVWH4GYCXDJHWTFDJQGF3RW","short_pith_number":"pith:4HPTVWH4","schema_version":"1.0","canonical_sha256":"e1df3ad8fc360571a4f699469818bb8d8d2224931d495e8c9e199d2042cea223","source":{"kind":"arxiv","id":"1211.6401","version":2},"attestation_state":"computed","paper":{"title":"On the Performance Bound of Sparse Estimation with Sensing Matrix Perturbation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Laming Chen, Yuantao Gu, Yujie Tang","submitted_at":"2012-11-27T19:46:15Z","abstract_excerpt":"This paper focusses on the sparse estimation in the situation where both the the sensing matrix and the measurement vector are corrupted by additive Gaussian noises. The performance bound of sparse estimation is analyzed and discussed in depth. Two types of lower bounds, the constrained Cram\\'{e}r-Rao bound (CCRB) and the Hammersley-Chapman-Robbins bound (HCRB), are discussed. It is shown that the situation with sensing matrix perturbation is more complex than the one with only measurement noise. For the CCRB, its closed-form expression is deduced. It demonstrates a gap between the maximal and"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1211.6401","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2012-11-27T19:46:15Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"df4d7dca3e3d01ca5f4d73ebc9168e5f2a67af613481fcdaf2bd89879d5b035b","abstract_canon_sha256":"7e28fdf046bdf049b7cf5654e9d97dffe92b2bdd14b267d509bf0d2f974d4f80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:52:54.256631Z","signature_b64":"g1zeMKvmC/CZAtOlSA8l66drcpmRtq8XJAwiUhXbrwvD1iv+rXNe1SH1XtmI+zBnxCvlerKeVgmKh8ZD4U/eCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1df3ad8fc360571a4f699469818bb8d8d2224931d495e8c9e199d2042cea223","last_reissued_at":"2026-05-18T01:52:54.255884Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:52:54.255884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Performance Bound of Sparse Estimation with Sensing Matrix Perturbation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Laming Chen, Yuantao Gu, Yujie Tang","submitted_at":"2012-11-27T19:46:15Z","abstract_excerpt":"This paper focusses on the sparse estimation in the situation where both the the sensing matrix and the measurement vector are corrupted by additive Gaussian noises. The performance bound of sparse estimation is analyzed and discussed in depth. Two types of lower bounds, the constrained Cram\\'{e}r-Rao bound (CCRB) and the Hammersley-Chapman-Robbins bound (HCRB), are discussed. It is shown that the situation with sensing matrix perturbation is more complex than the one with only measurement noise. For the CCRB, its closed-form expression is deduced. It demonstrates a gap between the maximal and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.6401","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1211.6401","created_at":"2026-05-18T01:52:54.256011+00:00"},{"alias_kind":"arxiv_version","alias_value":"1211.6401v2","created_at":"2026-05-18T01:52:54.256011+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.6401","created_at":"2026-05-18T01:52:54.256011+00:00"},{"alias_kind":"pith_short_12","alias_value":"4HPTVWH4GYCX","created_at":"2026-05-18T12:26:53.410803+00:00"},{"alias_kind":"pith_short_16","alias_value":"4HPTVWH4GYCXDJHW","created_at":"2026-05-18T12:26:53.410803+00:00"},{"alias_kind":"pith_short_8","alias_value":"4HPTVWH4","created_at":"2026-05-18T12:26:53.410803+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW","json":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW.json","graph_json":"https://pith.science/api/pith-number/4HPTVWH4GYCXDJHWTFDJQGF3RW/graph.json","events_json":"https://pith.science/api/pith-number/4HPTVWH4GYCXDJHWTFDJQGF3RW/events.json","paper":"https://pith.science/paper/4HPTVWH4"},"agent_actions":{"view_html":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW","download_json":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW.json","view_paper":"https://pith.science/paper/4HPTVWH4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1211.6401&json=true","fetch_graph":"https://pith.science/api/pith-number/4HPTVWH4GYCXDJHWTFDJQGF3RW/graph.json","fetch_events":"https://pith.science/api/pith-number/4HPTVWH4GYCXDJHWTFDJQGF3RW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW/action/storage_attestation","attest_author":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW/action/author_attestation","sign_citation":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW/action/citation_signature","submit_replication":"https://pith.science/pith/4HPTVWH4GYCXDJHWTFDJQGF3RW/action/replication_record"}},"created_at":"2026-05-18T01:52:54.256011+00:00","updated_at":"2026-05-18T01:52:54.256011+00:00"}