{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:EE2R23DD56Z7DCBQU2WPU3Y3BG","short_pith_number":"pith:EE2R23DD","schema_version":"1.0","canonical_sha256":"21351d6c63efb3f18830a6acfa6f1b099be8a17d28eefd126b0a0b9c9c267d2d","source":{"kind":"arxiv","id":"1908.06129","version":2},"attestation_state":"computed","paper":{"title":"Simultaneous estimation of normal means with side information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Sihai Dave Zhao","submitted_at":"2019-08-16T18:52:27Z","abstract_excerpt":"The integrative analysis of multiple datasets is an important strategy in data analysis. It is increasingly popular in genomics, which enjoys a wealth of publicly available datasets that can be compared, contrasted, and combined in order to extract novel scientific insights. This paper studies a stylized example of data integration for a classical statistical problem: leveraging side information to estimate a vector of normal means. This task is formulated as a compound decision problem, an oracle integrative decision rule is derived, and a data-driven estimate of this rule based on minimizing"},"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":"1908.06129","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-08-16T18:52:27Z","cross_cats_sorted":[],"title_canon_sha256":"ab0d080615c16b085b6cd37cd5c1f2c15e887ae19bb1e173fbed3cf749e3fe1b","abstract_canon_sha256":"7e0c0d798fc26847e3cdcca172f0be92a920013a336d3640aaebc84f49c0e496"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:20:18.721454Z","signature_b64":"j5vDM0wO8hvkSFvxO2E3nBH8O/CZzIZcMDtz23NhuZvyk4ygQzXlGTa/TXA/wMS+d6CSTODEBnKTlvA/54xjCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21351d6c63efb3f18830a6acfa6f1b099be8a17d28eefd126b0a0b9c9c267d2d","last_reissued_at":"2026-07-05T00:20:18.720964Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:20:18.720964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simultaneous estimation of normal means with side information","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Sihai Dave Zhao","submitted_at":"2019-08-16T18:52:27Z","abstract_excerpt":"The integrative analysis of multiple datasets is an important strategy in data analysis. It is increasingly popular in genomics, which enjoys a wealth of publicly available datasets that can be compared, contrasted, and combined in order to extract novel scientific insights. This paper studies a stylized example of data integration for a classical statistical problem: leveraging side information to estimate a vector of normal means. This task is formulated as a compound decision problem, an oracle integrative decision rule is derived, and a data-driven estimate of this rule based on minimizing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06129","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1908.06129/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1908.06129","created_at":"2026-07-05T00:20:18.721020+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.06129v2","created_at":"2026-07-05T00:20:18.721020+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06129","created_at":"2026-07-05T00:20:18.721020+00:00"},{"alias_kind":"pith_short_12","alias_value":"EE2R23DD56Z7","created_at":"2026-07-05T00:20:18.721020+00:00"},{"alias_kind":"pith_short_16","alias_value":"EE2R23DD56Z7DCBQ","created_at":"2026-07-05T00:20:18.721020+00:00"},{"alias_kind":"pith_short_8","alias_value":"EE2R23DD","created_at":"2026-07-05T00:20:18.721020+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/EE2R23DD56Z7DCBQU2WPU3Y3BG","json":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG.json","graph_json":"https://pith.science/api/pith-number/EE2R23DD56Z7DCBQU2WPU3Y3BG/graph.json","events_json":"https://pith.science/api/pith-number/EE2R23DD56Z7DCBQU2WPU3Y3BG/events.json","paper":"https://pith.science/paper/EE2R23DD"},"agent_actions":{"view_html":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG","download_json":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG.json","view_paper":"https://pith.science/paper/EE2R23DD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.06129&json=true","fetch_graph":"https://pith.science/api/pith-number/EE2R23DD56Z7DCBQU2WPU3Y3BG/graph.json","fetch_events":"https://pith.science/api/pith-number/EE2R23DD56Z7DCBQU2WPU3Y3BG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG/action/storage_attestation","attest_author":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG/action/author_attestation","sign_citation":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG/action/citation_signature","submit_replication":"https://pith.science/pith/EE2R23DD56Z7DCBQU2WPU3Y3BG/action/replication_record"}},"created_at":"2026-07-05T00:20:18.721020+00:00","updated_at":"2026-07-05T00:20:18.721020+00:00"}