{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:A5UJIS7QDFEDMFW2DFAOP5KPFO","short_pith_number":"pith:A5UJIS7Q","schema_version":"1.0","canonical_sha256":"0768944bf019483616da1940e7f54f2b8375025d157ebfc4d734df78045df10f","source":{"kind":"arxiv","id":"1605.04391","version":3},"attestation_state":"computed","paper":{"title":"Bayesian Lower Bounds for Dense or Sparse (Outlier) Noise in the RMT Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.OT"],"primary_cat":"stat.ME","authors_text":"Mohammed Nabil El Korso, Pascal Larzabal, R\\'emy Boyer, Virginie Ollier","submitted_at":"2016-05-14T08:31:57Z","abstract_excerpt":"Robust estimation is an important and timely research subject. In this paper, we investigate performance lower bounds on the mean-square-error (MSE) of any estimator for the Bayesian linear model, corrupted by a noise distributed according to an i.i.d. Student's t-distribution. This class of prior parametrized by its degree of freedom is relevant to modelize either dense or sparse (accounting for outliers) noise. Using the hierarchical Normal-Gamma representation of the Student's t-distribution, the Van Trees' Bayesian Cram\\'er-Rao bound (BCRB) on the amplitude parameters is derived. Furthermo"},"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":"1605.04391","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-05-14T08:31:57Z","cross_cats_sorted":["stat.OT"],"title_canon_sha256":"b405f4e39560fd80ebd019b5f8098d79589ecad904584591487bd8273a2a5f03","abstract_canon_sha256":"2ca9dd596cc9aef83306f5e669d1457d3f17521425acd8a93432ac9776c3ff5e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:33.595027Z","signature_b64":"giWnOlj8o3qzmNg+FuUR+E8Q++jTdF1voL5nBKE6yTHVIagobk9VwScEHrSfy/WSaWJLn4TadsCzjknqFbNiCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0768944bf019483616da1940e7f54f2b8375025d157ebfc4d734df78045df10f","last_reissued_at":"2026-05-18T00:40:33.594149Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:33.594149Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Lower Bounds for Dense or Sparse (Outlier) Noise in the RMT Framework","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.OT"],"primary_cat":"stat.ME","authors_text":"Mohammed Nabil El Korso, Pascal Larzabal, R\\'emy Boyer, Virginie Ollier","submitted_at":"2016-05-14T08:31:57Z","abstract_excerpt":"Robust estimation is an important and timely research subject. In this paper, we investigate performance lower bounds on the mean-square-error (MSE) of any estimator for the Bayesian linear model, corrupted by a noise distributed according to an i.i.d. Student's t-distribution. This class of prior parametrized by its degree of freedom is relevant to modelize either dense or sparse (accounting for outliers) noise. Using the hierarchical Normal-Gamma representation of the Student's t-distribution, the Van Trees' Bayesian Cram\\'er-Rao bound (BCRB) on the amplitude parameters is derived. Furthermo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.04391","kind":"arxiv","version":3},"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":"1605.04391","created_at":"2026-05-18T00:40:33.594301+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.04391v3","created_at":"2026-05-18T00:40:33.594301+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.04391","created_at":"2026-05-18T00:40:33.594301+00:00"},{"alias_kind":"pith_short_12","alias_value":"A5UJIS7QDFED","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_16","alias_value":"A5UJIS7QDFEDMFW2","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_8","alias_value":"A5UJIS7Q","created_at":"2026-05-18T12:30:04.600751+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/A5UJIS7QDFEDMFW2DFAOP5KPFO","json":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO.json","graph_json":"https://pith.science/api/pith-number/A5UJIS7QDFEDMFW2DFAOP5KPFO/graph.json","events_json":"https://pith.science/api/pith-number/A5UJIS7QDFEDMFW2DFAOP5KPFO/events.json","paper":"https://pith.science/paper/A5UJIS7Q"},"agent_actions":{"view_html":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO","download_json":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO.json","view_paper":"https://pith.science/paper/A5UJIS7Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.04391&json=true","fetch_graph":"https://pith.science/api/pith-number/A5UJIS7QDFEDMFW2DFAOP5KPFO/graph.json","fetch_events":"https://pith.science/api/pith-number/A5UJIS7QDFEDMFW2DFAOP5KPFO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO/action/storage_attestation","attest_author":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO/action/author_attestation","sign_citation":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO/action/citation_signature","submit_replication":"https://pith.science/pith/A5UJIS7QDFEDMFW2DFAOP5KPFO/action/replication_record"}},"created_at":"2026-05-18T00:40:33.594301+00:00","updated_at":"2026-05-18T00:40:33.594301+00:00"}