{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:U54VC6Q3ALZHPJTWU66Q26QC5F","short_pith_number":"pith:U54VC6Q3","schema_version":"1.0","canonical_sha256":"a779517a1b02f277a676a7bd0d7a02e97a0a655f73613539307e7f921046037d","source":{"kind":"arxiv","id":"1509.07942","version":2},"attestation_state":"computed","paper":{"title":"Bayesian Estimators in Uncertain Nested Error Regression Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Shonosuke Sugasawa, Tatsuya Kubokawa","submitted_at":"2015-09-26T03:39:49Z","abstract_excerpt":"Nested error regression models are useful tools for analysis of grouped data, especially in the case of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in each area is expressed as a mixture of a normal distribution and a positive mass at $0$. For estimation of the model parameters and prediction of the random effects, an objective Bayesian inference is proposed by setting non-informative prior distributions on the model parameters. Under mild sufficient conditions, it is shown that the posterior distribution "},"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":"1509.07942","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-09-26T03:39:49Z","cross_cats_sorted":[],"title_canon_sha256":"4cdbea8b6a8809c7a2909d550577cf468904f491010060b44d40d308a08f36a6","abstract_canon_sha256":"64a93d7b620a5a60ef675193159861b0f0deefdb567c884108f279a8496d6925"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:01.877101Z","signature_b64":"Eapr96l4AX6bIzwEVHxozhQU6fJZjudQwQiAhuQqETQdH+qXuOruX8qdKJv91+zH85OiyTXkg7xrAK6R8eW9Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a779517a1b02f277a676a7bd0d7a02e97a0a655f73613539307e7f921046037d","last_reissued_at":"2026-05-18T00:50:01.876460Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:01.876460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Estimators in Uncertain Nested Error Regression Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Shonosuke Sugasawa, Tatsuya Kubokawa","submitted_at":"2015-09-26T03:39:49Z","abstract_excerpt":"Nested error regression models are useful tools for analysis of grouped data, especially in the case of small area estimation. This paper suggests a nested error regression model using uncertain random effects in which the random effect in each area is expressed as a mixture of a normal distribution and a positive mass at $0$. For estimation of the model parameters and prediction of the random effects, an objective Bayesian inference is proposed by setting non-informative prior distributions on the model parameters. Under mild sufficient conditions, it is shown that the posterior distribution "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.07942","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":"1509.07942","created_at":"2026-05-18T00:50:01.876558+00:00"},{"alias_kind":"arxiv_version","alias_value":"1509.07942v2","created_at":"2026-05-18T00:50:01.876558+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.07942","created_at":"2026-05-18T00:50:01.876558+00:00"},{"alias_kind":"pith_short_12","alias_value":"U54VC6Q3ALZH","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_16","alias_value":"U54VC6Q3ALZHPJTW","created_at":"2026-05-18T12:29:44.643036+00:00"},{"alias_kind":"pith_short_8","alias_value":"U54VC6Q3","created_at":"2026-05-18T12:29:44.643036+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/U54VC6Q3ALZHPJTWU66Q26QC5F","json":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F.json","graph_json":"https://pith.science/api/pith-number/U54VC6Q3ALZHPJTWU66Q26QC5F/graph.json","events_json":"https://pith.science/api/pith-number/U54VC6Q3ALZHPJTWU66Q26QC5F/events.json","paper":"https://pith.science/paper/U54VC6Q3"},"agent_actions":{"view_html":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F","download_json":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F.json","view_paper":"https://pith.science/paper/U54VC6Q3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1509.07942&json=true","fetch_graph":"https://pith.science/api/pith-number/U54VC6Q3ALZHPJTWU66Q26QC5F/graph.json","fetch_events":"https://pith.science/api/pith-number/U54VC6Q3ALZHPJTWU66Q26QC5F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F/action/storage_attestation","attest_author":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F/action/author_attestation","sign_citation":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F/action/citation_signature","submit_replication":"https://pith.science/pith/U54VC6Q3ALZHPJTWU66Q26QC5F/action/replication_record"}},"created_at":"2026-05-18T00:50:01.876558+00:00","updated_at":"2026-05-18T00:50:01.876558+00:00"}