{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:FN654SCJEAPQLG5CUXZ7R6M3EL","short_pith_number":"pith:FN654SCJ","schema_version":"1.0","canonical_sha256":"2b7dde4849201f059ba2a5f3f8f99b22da6ccac3cc32dccf0a39db59186fe285","source":{"kind":"arxiv","id":"1406.6419","version":2},"attestation_state":"computed","paper":{"title":"Block Hyper-g Priors in Bayesian Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Agniva Som, Christopher M. Hans, Steven N. MacEachern","submitted_at":"2014-06-25T00:04:50Z","abstract_excerpt":"The development of prior distributions for Bayesian regression has traditionally been driven by the goal of achieving sensible model selection and parameter estimation. The formalization of properties that characterize good performance has led to the development and popularization of thick tailed mixtures of g priors such as the Zellner--Siow and hyper-g priors. The properties of a particular prior are typically illuminated under limits on the likelihood or the prior. In this paper we introduce a new, conditional information asymptotic that is motivated by the common data analysis setting wher"},"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":"1406.6419","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-06-25T00:04:50Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"2ce89c100f240e1e651d23475144795800eb78e9f495f65ae6ffa9d5276138e3","abstract_canon_sha256":"80279367a93cb286654a9c41c396a21c55227c7946be9022597286808dd9f619"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:29:32.055789Z","signature_b64":"NPmmxuiBOI+9mSdjlItTLvOgIo0eIdvAC9JbXVWBlBVB2TASROlhEH4JNdTUi/ErrYbGYlsJOEvlWlRZ766RAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b7dde4849201f059ba2a5f3f8f99b22da6ccac3cc32dccf0a39db59186fe285","last_reissued_at":"2026-05-18T02:29:32.055284Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:29:32.055284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Block Hyper-g Priors in Bayesian Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Agniva Som, Christopher M. Hans, Steven N. MacEachern","submitted_at":"2014-06-25T00:04:50Z","abstract_excerpt":"The development of prior distributions for Bayesian regression has traditionally been driven by the goal of achieving sensible model selection and parameter estimation. The formalization of properties that characterize good performance has led to the development and popularization of thick tailed mixtures of g priors such as the Zellner--Siow and hyper-g priors. The properties of a particular prior are typically illuminated under limits on the likelihood or the prior. In this paper we introduce a new, conditional information asymptotic that is motivated by the common data analysis setting wher"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.6419","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":"1406.6419","created_at":"2026-05-18T02:29:32.055366+00:00"},{"alias_kind":"arxiv_version","alias_value":"1406.6419v2","created_at":"2026-05-18T02:29:32.055366+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.6419","created_at":"2026-05-18T02:29:32.055366+00:00"},{"alias_kind":"pith_short_12","alias_value":"FN654SCJEAPQ","created_at":"2026-05-18T12:28:28.263976+00:00"},{"alias_kind":"pith_short_16","alias_value":"FN654SCJEAPQLG5C","created_at":"2026-05-18T12:28:28.263976+00:00"},{"alias_kind":"pith_short_8","alias_value":"FN654SCJ","created_at":"2026-05-18T12:28:28.263976+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/FN654SCJEAPQLG5CUXZ7R6M3EL","json":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL.json","graph_json":"https://pith.science/api/pith-number/FN654SCJEAPQLG5CUXZ7R6M3EL/graph.json","events_json":"https://pith.science/api/pith-number/FN654SCJEAPQLG5CUXZ7R6M3EL/events.json","paper":"https://pith.science/paper/FN654SCJ"},"agent_actions":{"view_html":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL","download_json":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL.json","view_paper":"https://pith.science/paper/FN654SCJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1406.6419&json=true","fetch_graph":"https://pith.science/api/pith-number/FN654SCJEAPQLG5CUXZ7R6M3EL/graph.json","fetch_events":"https://pith.science/api/pith-number/FN654SCJEAPQLG5CUXZ7R6M3EL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL/action/storage_attestation","attest_author":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL/action/author_attestation","sign_citation":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL/action/citation_signature","submit_replication":"https://pith.science/pith/FN654SCJEAPQLG5CUXZ7R6M3EL/action/replication_record"}},"created_at":"2026-05-18T02:29:32.055366+00:00","updated_at":"2026-05-18T02:29:32.055366+00:00"}