{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:3TZZY3YBJODNVQJ54SG26O2O3Q","short_pith_number":"pith:3TZZY3YB","schema_version":"1.0","canonical_sha256":"dcf39c6f014b86dac13de48daf3b4edc0f2d5f546d66f1fc35e6f358f4708f4f","source":{"kind":"arxiv","id":"1606.03749","version":2},"attestation_state":"computed","paper":{"title":"Scalable Bayesian variable selection and model averaging under block orthogonal design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"David Rossell, Omiros Papaspiliopoulos","submitted_at":"2016-06-12T18:35:38Z","abstract_excerpt":"We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for block-diagonal designs. Posterior probabilities for any number of models are obtained by evaluating a sin"},"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":"1606.03749","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-06-12T18:35:38Z","cross_cats_sorted":[],"title_canon_sha256":"43d10118248e7ab31757a8b08e131d4716d981ff1235a909c5f7336ce2097ef5","abstract_canon_sha256":"c1b9adf16e9cf09f79931b4a6b40b65404ab35adcdb1a1fd5d2bb6fe46cd943f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:34.040683Z","signature_b64":"03oqe2SwczgkhRUEwZWQrF049lWAdaOxFEaLXV2qALbTTvqwJ9KRD5Lt6wx3KEsyj6IY7y0oP6a16VyjZ7vgBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcf39c6f014b86dac13de48daf3b4edc0f2d5f546d66f1fc35e6f358f4708f4f","last_reissued_at":"2026-05-18T00:53:34.040168Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:34.040168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Bayesian variable selection and model averaging under block orthogonal design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"David Rossell, Omiros Papaspiliopoulos","submitted_at":"2016-06-12T18:35:38Z","abstract_excerpt":"We propose a scalable algorithmic framework for exact Bayesian variable selection and model averaging in linear models under the assumption that the Gram matrix is block-diagonal, and as a heuristic for exploring the model space for general designs. In block-diagonal designs our approach returns the most probable model of any given size without resorting to numerical integration. The algorithm also provides a novel and efficient solution to the frequentist best subset selection problem for block-diagonal designs. Posterior probabilities for any number of models are obtained by evaluating a sin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.03749","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":"1606.03749","created_at":"2026-05-18T00:53:34.040250+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.03749v2","created_at":"2026-05-18T00:53:34.040250+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.03749","created_at":"2026-05-18T00:53:34.040250+00:00"},{"alias_kind":"pith_short_12","alias_value":"3TZZY3YBJODN","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_16","alias_value":"3TZZY3YBJODNVQJ5","created_at":"2026-05-18T12:29:58.707656+00:00"},{"alias_kind":"pith_short_8","alias_value":"3TZZY3YB","created_at":"2026-05-18T12:29:58.707656+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/3TZZY3YBJODNVQJ54SG26O2O3Q","json":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q.json","graph_json":"https://pith.science/api/pith-number/3TZZY3YBJODNVQJ54SG26O2O3Q/graph.json","events_json":"https://pith.science/api/pith-number/3TZZY3YBJODNVQJ54SG26O2O3Q/events.json","paper":"https://pith.science/paper/3TZZY3YB"},"agent_actions":{"view_html":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q","download_json":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q.json","view_paper":"https://pith.science/paper/3TZZY3YB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.03749&json=true","fetch_graph":"https://pith.science/api/pith-number/3TZZY3YBJODNVQJ54SG26O2O3Q/graph.json","fetch_events":"https://pith.science/api/pith-number/3TZZY3YBJODNVQJ54SG26O2O3Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q/action/storage_attestation","attest_author":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q/action/author_attestation","sign_citation":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q/action/citation_signature","submit_replication":"https://pith.science/pith/3TZZY3YBJODNVQJ54SG26O2O3Q/action/replication_record"}},"created_at":"2026-05-18T00:53:34.040250+00:00","updated_at":"2026-05-18T00:53:34.040250+00:00"}