{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:XKKB7AW7ZXPVXDCO72MFWKZIC7","short_pith_number":"pith:XKKB7AW7","canonical_record":{"source":{"id":"1905.05242","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-13T18:56:36Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"4c6c47ef504050c4b8aa463dc9408b91ecd2678dead5888b57ba0c1b2d0d38b7","abstract_canon_sha256":"4e4ab246d83729eb2dab84e31ded675a4b71e2c3a4adb33bc7ad9d1b1036b279"},"schema_version":"1.0"},"canonical_sha256":"ba941f82dfcddf5b8c4efe985b2b2817fc3d3299169c7471b9a6e1aa940cfc63","source":{"kind":"arxiv","id":"1905.05242","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05242","created_at":"2026-05-17T23:46:17Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05242v1","created_at":"2026-05-17T23:46:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05242","created_at":"2026-05-17T23:46:17Z"},{"alias_kind":"pith_short_12","alias_value":"XKKB7AW7ZXPV","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XKKB7AW7ZXPVXDCO","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XKKB7AW7","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:XKKB7AW7ZXPVXDCO72MFWKZIC7","target":"record","payload":{"canonical_record":{"source":{"id":"1905.05242","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-13T18:56:36Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"4c6c47ef504050c4b8aa463dc9408b91ecd2678dead5888b57ba0c1b2d0d38b7","abstract_canon_sha256":"4e4ab246d83729eb2dab84e31ded675a4b71e2c3a4adb33bc7ad9d1b1036b279"},"schema_version":"1.0"},"canonical_sha256":"ba941f82dfcddf5b8c4efe985b2b2817fc3d3299169c7471b9a6e1aa940cfc63","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:17.076628Z","signature_b64":"Uz8WCiE4GB9rGCwULucCwWouJfTiYwR+qnSZBCi9LyKDNtVuMuC40Q5dA2p5dTt3x7mS9mRvFHenXMJt3vrcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ba941f82dfcddf5b8c4efe985b2b2817fc3d3299169c7471b9a6e1aa940cfc63","last_reissued_at":"2026-05-17T23:46:17.076004Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:17.076004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.05242","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J8U1hWGrkPmZPCAgeBtlJoPXuEon8S0U8bm4XyfjNGLePhJ2eKnPGTdovgsFX9J6nuqwtok6f2FpGlmHHipfDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T01:51:55.363200Z"},"content_sha256":"d955c8c8f8c4b40f8111d08612850ff84c151a302e3d82d66a597c925bcd84bb","schema_version":"1.0","event_id":"sha256:d955c8c8f8c4b40f8111d08612850ff84c151a302e3d82d66a597c925bcd84bb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:XKKB7AW7ZXPVXDCO72MFWKZIC7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical approaches for flexible and interpretable binary regression models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.ME","authors_text":"Henry R. Scharf, Mevin B. Hooten, Perry J. Williams, Xinyi Lu","submitted_at":"2019-05-13T18:56:36Z","abstract_excerpt":"Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalized linear models fail to capture the variability in the probability surface that gives rise to the binary observations and novel methodology is required. This has generated a substantial literature comprised of binary regression models motivated by various applications. We describe a novel organization of generalizations to traditional binary regression methods based on the familiar three-part structure of generalized linear models (random component, systematic component, link function"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05242","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:46:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mum1QCrTup/tBxaUXFn4S9SOGLaCNyUB2MkiR6P9eX08GMXeU0qaDdu6gh0LeMeEVWEkEUp1yHvQOIhhWhG5Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T01:51:55.364183Z"},"content_sha256":"4c17756a25bb9e3c37d2ea22b07d7a2ebdef6db9d7cc1aeb248f7dffddde4e76","schema_version":"1.0","event_id":"sha256:4c17756a25bb9e3c37d2ea22b07d7a2ebdef6db9d7cc1aeb248f7dffddde4e76"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XKKB7AW7ZXPVXDCO72MFWKZIC7/bundle.json","state_url":"https://pith.science/pith/XKKB7AW7ZXPVXDCO72MFWKZIC7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XKKB7AW7ZXPVXDCO72MFWKZIC7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-01T01:51:55Z","links":{"resolver":"https://pith.science/pith/XKKB7AW7ZXPVXDCO72MFWKZIC7","bundle":"https://pith.science/pith/XKKB7AW7ZXPVXDCO72MFWKZIC7/bundle.json","state":"https://pith.science/pith/XKKB7AW7ZXPVXDCO72MFWKZIC7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XKKB7AW7ZXPVXDCO72MFWKZIC7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:XKKB7AW7ZXPVXDCO72MFWKZIC7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4e4ab246d83729eb2dab84e31ded675a4b71e2c3a4adb33bc7ad9d1b1036b279","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-13T18:56:36Z","title_canon_sha256":"4c6c47ef504050c4b8aa463dc9408b91ecd2678dead5888b57ba0c1b2d0d38b7"},"schema_version":"1.0","source":{"id":"1905.05242","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.05242","created_at":"2026-05-17T23:46:17Z"},{"alias_kind":"arxiv_version","alias_value":"1905.05242v1","created_at":"2026-05-17T23:46:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05242","created_at":"2026-05-17T23:46:17Z"},{"alias_kind":"pith_short_12","alias_value":"XKKB7AW7ZXPV","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"XKKB7AW7ZXPVXDCO","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"XKKB7AW7","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:4c17756a25bb9e3c37d2ea22b07d7a2ebdef6db9d7cc1aeb248f7dffddde4e76","target":"graph","created_at":"2026-05-17T23:46:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalized linear models fail to capture the variability in the probability surface that gives rise to the binary observations and novel methodology is required. This has generated a substantial literature comprised of binary regression models motivated by various applications. We describe a novel organization of generalizations to traditional binary regression methods based on the familiar three-part structure of generalized linear models (random component, systematic component, link function","authors_text":"Henry R. Scharf, Mevin B. Hooten, Perry J. Williams, Xinyi Lu","cross_cats":["stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-13T18:56:36Z","title":"Hierarchical approaches for flexible and interpretable binary regression models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05242","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d955c8c8f8c4b40f8111d08612850ff84c151a302e3d82d66a597c925bcd84bb","target":"record","created_at":"2026-05-17T23:46:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4e4ab246d83729eb2dab84e31ded675a4b71e2c3a4adb33bc7ad9d1b1036b279","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-05-13T18:56:36Z","title_canon_sha256":"4c6c47ef504050c4b8aa463dc9408b91ecd2678dead5888b57ba0c1b2d0d38b7"},"schema_version":"1.0","source":{"id":"1905.05242","kind":"arxiv","version":1}},"canonical_sha256":"ba941f82dfcddf5b8c4efe985b2b2817fc3d3299169c7471b9a6e1aa940cfc63","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ba941f82dfcddf5b8c4efe985b2b2817fc3d3299169c7471b9a6e1aa940cfc63","first_computed_at":"2026-05-17T23:46:17.076004Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:46:17.076004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Uz8WCiE4GB9rGCwULucCwWouJfTiYwR+qnSZBCi9LyKDNtVuMuC40Q5dA2p5dTt3x7mS9mRvFHenXMJt3vrcBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:46:17.076628Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.05242","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d955c8c8f8c4b40f8111d08612850ff84c151a302e3d82d66a597c925bcd84bb","sha256:4c17756a25bb9e3c37d2ea22b07d7a2ebdef6db9d7cc1aeb248f7dffddde4e76"],"state_sha256":"e1012b78e7cc37a14cc11e268c9582dad4fb04a212b1a0930fee899e9f86f3f4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MSHIIVaCoVz4Adw0TC7260zdys1e6GV8txzLHBrNbrWLp3ZikfvcoRQg1CdjtdQ/Ed71WsRnoMdnGEKerImcAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T01:51:55.368776Z","bundle_sha256":"bcb2e465819eb41de9fcfa0a5b58f7bddda060131e52919fc781c90cbfb8d075"}}