{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:SIKWU5EFMTLVJUNMSE7J2YNW5K","short_pith_number":"pith:SIKWU5EF","schema_version":"1.0","canonical_sha256":"92156a748564d754d1ac913e9d61b6eab6a5307bd427cdfd7ef94fc985418881","source":{"kind":"arxiv","id":"1602.00542","version":4},"attestation_state":"computed","paper":{"title":"Cluster-Seeking James-Stein Estimators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.IT","authors_text":"K. Pavan Srinath, Ramji Venkataramanan","submitted_at":"2016-02-01T14:37:20Z","abstract_excerpt":"This paper considers the problem of estimating a high-dimensional vector of parameters $\\boldsymbol{\\theta} \\in \\mathbb{R}^n$ from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (ML) estimator when the dimension $n$ exceeds two. The JS-estimator shrinks the observed vector towards the origin, and the risk reduction over the ML-estimator is greatest for $\\boldsymbol{\\theta}$ that lie close to the origin. JS-estimators can be generalized to shrink th"},"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":"1602.00542","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-02-01T14:37:20Z","cross_cats_sorted":["math.IT","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"b0c421edd237e002f11eb8fae2f8d75d4e461c76af1ae4170565428da71a26eb","abstract_canon_sha256":"d9d5b7db854a945792b9406b6c3d98107e410bafed84548305a93f157f0a8128"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:52.624908Z","signature_b64":"X4V9iEjc9rapcJsWwtjGlHBb28Lc8L07jkVfHfvOoGk1UoZXbqGzlUH4xSosNvYBQ02ESrJ58t2hwE7hYqxxDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92156a748564d754d1ac913e9d61b6eab6a5307bd427cdfd7ef94fc985418881","last_reissued_at":"2026-05-18T00:20:52.624506Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:52.624506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cluster-Seeking James-Stein Estimators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT","math.ST","stat.ML","stat.TH"],"primary_cat":"cs.IT","authors_text":"K. Pavan Srinath, Ramji Venkataramanan","submitted_at":"2016-02-01T14:37:20Z","abstract_excerpt":"This paper considers the problem of estimating a high-dimensional vector of parameters $\\boldsymbol{\\theta} \\in \\mathbb{R}^n$ from a noisy observation. The noise vector is i.i.d. Gaussian with known variance. For a squared-error loss function, the James-Stein (JS) estimator is known to dominate the simple maximum-likelihood (ML) estimator when the dimension $n$ exceeds two. The JS-estimator shrinks the observed vector towards the origin, and the risk reduction over the ML-estimator is greatest for $\\boldsymbol{\\theta}$ that lie close to the origin. JS-estimators can be generalized to shrink th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.00542","kind":"arxiv","version":4},"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":"1602.00542","created_at":"2026-05-18T00:20:52.624573+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.00542v4","created_at":"2026-05-18T00:20:52.624573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.00542","created_at":"2026-05-18T00:20:52.624573+00:00"},{"alias_kind":"pith_short_12","alias_value":"SIKWU5EFMTLV","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_16","alias_value":"SIKWU5EFMTLVJUNM","created_at":"2026-05-18T12:30:44.179134+00:00"},{"alias_kind":"pith_short_8","alias_value":"SIKWU5EF","created_at":"2026-05-18T12:30:44.179134+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/SIKWU5EFMTLVJUNMSE7J2YNW5K","json":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K.json","graph_json":"https://pith.science/api/pith-number/SIKWU5EFMTLVJUNMSE7J2YNW5K/graph.json","events_json":"https://pith.science/api/pith-number/SIKWU5EFMTLVJUNMSE7J2YNW5K/events.json","paper":"https://pith.science/paper/SIKWU5EF"},"agent_actions":{"view_html":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K","download_json":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K.json","view_paper":"https://pith.science/paper/SIKWU5EF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.00542&json=true","fetch_graph":"https://pith.science/api/pith-number/SIKWU5EFMTLVJUNMSE7J2YNW5K/graph.json","fetch_events":"https://pith.science/api/pith-number/SIKWU5EFMTLVJUNMSE7J2YNW5K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K/action/storage_attestation","attest_author":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K/action/author_attestation","sign_citation":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K/action/citation_signature","submit_replication":"https://pith.science/pith/SIKWU5EFMTLVJUNMSE7J2YNW5K/action/replication_record"}},"created_at":"2026-05-18T00:20:52.624573+00:00","updated_at":"2026-05-18T00:20:52.624573+00:00"}