{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:PY3GPO7SP46UMSTJKMEB7NTGRB","short_pith_number":"pith:PY3GPO7S","schema_version":"1.0","canonical_sha256":"7e3667bbf27f3d464a6953081fb666884531026e94612c081106fb7fc56428f4","source":{"kind":"arxiv","id":"1009.3353","version":1},"attestation_state":"computed","paper":{"title":"A Lower Bound on the Estimator Variance for the Sparse Linear Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander Jung, Franz Hlawatsch, Sebastian Schmutzhard, Yonina C. Eldar, Zvika Ben-Haim","submitted_at":"2010-09-17T07:38:37Z","abstract_excerpt":"We study the performance of estimators of a sparse nonrandom vector based on an observation which is linearly transformed and corrupted by additive white Gaussian noise. Using the reproducing kernel Hilbert space framework, we derive a new lower bound on the estimator variance for a given differentiable bias function (including the unbiased case) and an almost arbitrary transformation matrix (including the underdetermined case considered in compressed sensing theory). For the special case of a sparse vector corrupted by white Gaussian noise-i.e., without a linear transformation-and unbiased es"},"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":"1009.3353","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2010-09-17T07:38:37Z","cross_cats_sorted":["cs.IT","math.IT","stat.TH"],"title_canon_sha256":"5105cb412bba65c1963f339c131724eae8d421c8b933d062ab157a081c9afe7e","abstract_canon_sha256":"5ec0dbf34e709a95c7f5103613224701497134693ad5fa8515cce144ca2ef873"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:40:45.881390Z","signature_b64":"dJwtsRq/q7DaFAGYTp1zYECQ0bLOQJoG+mT9mWj8/B4bx8TXyQlQWPA/e/LW5TmEMHo6VgyB93CajyA7KsgMCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e3667bbf27f3d464a6953081fb666884531026e94612c081106fb7fc56428f4","last_reissued_at":"2026-05-18T04:40:45.880636Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:40:45.880636Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Lower Bound on the Estimator Variance for the Sparse Linear Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","math.IT","stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander Jung, Franz Hlawatsch, Sebastian Schmutzhard, Yonina C. Eldar, Zvika Ben-Haim","submitted_at":"2010-09-17T07:38:37Z","abstract_excerpt":"We study the performance of estimators of a sparse nonrandom vector based on an observation which is linearly transformed and corrupted by additive white Gaussian noise. Using the reproducing kernel Hilbert space framework, we derive a new lower bound on the estimator variance for a given differentiable bias function (including the unbiased case) and an almost arbitrary transformation matrix (including the underdetermined case considered in compressed sensing theory). For the special case of a sparse vector corrupted by white Gaussian noise-i.e., without a linear transformation-and unbiased es"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1009.3353","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1009.3353","created_at":"2026-05-18T04:40:45.880762+00:00"},{"alias_kind":"arxiv_version","alias_value":"1009.3353v1","created_at":"2026-05-18T04:40:45.880762+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1009.3353","created_at":"2026-05-18T04:40:45.880762+00:00"},{"alias_kind":"pith_short_12","alias_value":"PY3GPO7SP46U","created_at":"2026-05-18T12:26:12.377268+00:00"},{"alias_kind":"pith_short_16","alias_value":"PY3GPO7SP46UMSTJ","created_at":"2026-05-18T12:26:12.377268+00:00"},{"alias_kind":"pith_short_8","alias_value":"PY3GPO7S","created_at":"2026-05-18T12:26:12.377268+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/PY3GPO7SP46UMSTJKMEB7NTGRB","json":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB.json","graph_json":"https://pith.science/api/pith-number/PY3GPO7SP46UMSTJKMEB7NTGRB/graph.json","events_json":"https://pith.science/api/pith-number/PY3GPO7SP46UMSTJKMEB7NTGRB/events.json","paper":"https://pith.science/paper/PY3GPO7S"},"agent_actions":{"view_html":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB","download_json":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB.json","view_paper":"https://pith.science/paper/PY3GPO7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1009.3353&json=true","fetch_graph":"https://pith.science/api/pith-number/PY3GPO7SP46UMSTJKMEB7NTGRB/graph.json","fetch_events":"https://pith.science/api/pith-number/PY3GPO7SP46UMSTJKMEB7NTGRB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB/action/storage_attestation","attest_author":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB/action/author_attestation","sign_citation":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB/action/citation_signature","submit_replication":"https://pith.science/pith/PY3GPO7SP46UMSTJKMEB7NTGRB/action/replication_record"}},"created_at":"2026-05-18T04:40:45.880762+00:00","updated_at":"2026-05-18T04:40:45.880762+00:00"}