{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:RWH7NFJARJBD7PU6KZNW55QAI6","short_pith_number":"pith:RWH7NFJA","schema_version":"1.0","canonical_sha256":"8d8ff695208a423fbe9e565b6ef60047b70cfdda749cd23e28b8bb03e5e2ab21","source":{"kind":"arxiv","id":"1303.3390","version":1},"attestation_state":"computed","paper":{"title":"Conjugate distributions in hierarchical Bayesian ANOVA for computational efficiency and assessments of both practical and statistical significance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Reinhard Furrer, Steven Geinitz","submitted_at":"2013-03-14T10:03:50Z","abstract_excerpt":"Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to the case where all levels of a factor are present in the data (i.e. the population of factor levels has been exhausted). Random and mixed effects models are used for more elaborate cases, but require distinct nomenclature, concepts and theory, as well as distinct inferential procedures. Following a hierarchical Bayesian approach, a comprehensive ANOVA framework is shown, which unifies the above s"},"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":"1303.3390","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-03-14T10:03:50Z","cross_cats_sorted":[],"title_canon_sha256":"9ac7e4f678b22fa3b681c04e9990de97e80fe5d5f894022be1bde90ab0060d93","abstract_canon_sha256":"d6d6034f0da1fe2e2384dbd0e86c808c46c260624251d9bab58619a6f547625e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:30:54.681596Z","signature_b64":"/AzTihRYcfhgW2AgnNlEAVXq4k/mQnFlxsld5VqTrTY4DWZnhfpCGVdPsO1SqNEMX6cHqnGA+4NyJyCvkTdGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d8ff695208a423fbe9e565b6ef60047b70cfdda749cd23e28b8bb03e5e2ab21","last_reissued_at":"2026-05-18T03:30:54.681145Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:30:54.681145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conjugate distributions in hierarchical Bayesian ANOVA for computational efficiency and assessments of both practical and statistical significance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Reinhard Furrer, Steven Geinitz","submitted_at":"2013-03-14T10:03:50Z","abstract_excerpt":"Assessing variability according to distinct factors in data is a fundamental technique of statistics. The method commonly regarded to as analysis of variance (ANOVA) is, however, typically confined to the case where all levels of a factor are present in the data (i.e. the population of factor levels has been exhausted). Random and mixed effects models are used for more elaborate cases, but require distinct nomenclature, concepts and theory, as well as distinct inferential procedures. Following a hierarchical Bayesian approach, a comprehensive ANOVA framework is shown, which unifies the above s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.3390","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":"1303.3390","created_at":"2026-05-18T03:30:54.681207+00:00"},{"alias_kind":"arxiv_version","alias_value":"1303.3390v1","created_at":"2026-05-18T03:30:54.681207+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.3390","created_at":"2026-05-18T03:30:54.681207+00:00"},{"alias_kind":"pith_short_12","alias_value":"RWH7NFJARJBD","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_16","alias_value":"RWH7NFJARJBD7PU6","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_8","alias_value":"RWH7NFJA","created_at":"2026-05-18T12:27:59.945178+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/RWH7NFJARJBD7PU6KZNW55QAI6","json":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6.json","graph_json":"https://pith.science/api/pith-number/RWH7NFJARJBD7PU6KZNW55QAI6/graph.json","events_json":"https://pith.science/api/pith-number/RWH7NFJARJBD7PU6KZNW55QAI6/events.json","paper":"https://pith.science/paper/RWH7NFJA"},"agent_actions":{"view_html":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6","download_json":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6.json","view_paper":"https://pith.science/paper/RWH7NFJA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1303.3390&json=true","fetch_graph":"https://pith.science/api/pith-number/RWH7NFJARJBD7PU6KZNW55QAI6/graph.json","fetch_events":"https://pith.science/api/pith-number/RWH7NFJARJBD7PU6KZNW55QAI6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6/action/storage_attestation","attest_author":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6/action/author_attestation","sign_citation":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6/action/citation_signature","submit_replication":"https://pith.science/pith/RWH7NFJARJBD7PU6KZNW55QAI6/action/replication_record"}},"created_at":"2026-05-18T03:30:54.681207+00:00","updated_at":"2026-05-18T03:30:54.681207+00:00"}