{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Z4SCVC4UDJLCWY6T56KTEOYRZP","short_pith_number":"pith:Z4SCVC4U","canonical_record":{"source":{"id":"2208.12929","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-08-27T05:18:44Z","cross_cats_sorted":[],"title_canon_sha256":"2944892d79079e6143504660ed30645a82ae833e908fa73e2037db683dee42ec","abstract_canon_sha256":"d4c9a0580722038e9c840673926bf6325127823754da36bc823450b3ee3f1350"},"schema_version":"1.0"},"canonical_sha256":"cf242a8b941a562b63d3ef95323b11cbefe8e58de167531ebf7ee45ad05adca2","source":{"kind":"arxiv","id":"2208.12929","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.12929","created_at":"2026-05-18T03:09:47Z"},{"alias_kind":"arxiv_version","alias_value":"2208.12929v1","created_at":"2026-05-18T03:09:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.12929","created_at":"2026-05-18T03:09:47Z"},{"alias_kind":"pith_short_12","alias_value":"Z4SCVC4UDJLC","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Z4SCVC4UDJLCWY6T","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Z4SCVC4U","created_at":"2026-05-18T12:33:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Z4SCVC4UDJLCWY6T56KTEOYRZP","target":"record","payload":{"canonical_record":{"source":{"id":"2208.12929","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-08-27T05:18:44Z","cross_cats_sorted":[],"title_canon_sha256":"2944892d79079e6143504660ed30645a82ae833e908fa73e2037db683dee42ec","abstract_canon_sha256":"d4c9a0580722038e9c840673926bf6325127823754da36bc823450b3ee3f1350"},"schema_version":"1.0"},"canonical_sha256":"cf242a8b941a562b63d3ef95323b11cbefe8e58de167531ebf7ee45ad05adca2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:47.481291Z","signature_b64":"O/HQ7ymlfBz07W6MBYpz2haEpmzYW38s6AWLbVgGfVBtO2g5o0VJV7Ng1CJ6LEGfAcMXlegurkroInw6gJ2DCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf242a8b941a562b63d3ef95323b11cbefe8e58de167531ebf7ee45ad05adca2","last_reissued_at":"2026-05-18T03:09:47.480429Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:47.480429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2208.12929","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":"2026-05-18T03:09:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ixJ8Jw0mijQILr3EqnKcvWGBMQnjVXNtnWrJ7Zl2AWAprziT2ngh17P/4hSYomj75NYAfRMDDapEQhxLGSXWAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T10:52:33.930969Z"},"content_sha256":"44d87ad750bde452148d4f855b740babbc4b53b48a5bf56fcfd718e253c61bd5","schema_version":"1.0","event_id":"sha256:44d87ad750bde452148d4f855b740babbc4b53b48a5bf56fcfd718e253c61bd5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Z4SCVC4UDJLCWY6T56KTEOYRZP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Gerko Vink, Mingyang Cai, Stef van Buuren","submitted_at":"2022-08-27T05:18:44Z","abstract_excerpt":"Missing data are often dealt with multiple imputation. A crucial part of the multiple imputation process is selecting sensible models to generate plausible values for incomplete data. A method based on posterior predictive checking is proposed to diagnose imputation models based on posterior predictive checking. To assess the congeniality of imputation models, the proposed diagnostic method compares the observed data with their replicates generated under corresponding posterior predictive distributions. If the imputation model is congenial with the substantive model, the observed data are expe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.12929","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":"2026-05-18T03:09:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LblyRL6ukLsgul7BH4zADbSWXHNqxGC86zk8m/Nxwz55U7yvnb6DiCn7zbSrn0bVjuiPNBx6RjkPve3vKybjAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T10:52:33.931623Z"},"content_sha256":"87e69d2bad26e846be5ba03f93144aef958502d0c058fad2cb8ef434a739273e","schema_version":"1.0","event_id":"sha256:87e69d2bad26e846be5ba03f93144aef958502d0c058fad2cb8ef434a739273e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z4SCVC4UDJLCWY6T56KTEOYRZP/bundle.json","state_url":"https://pith.science/pith/Z4SCVC4UDJLCWY6T56KTEOYRZP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z4SCVC4UDJLCWY6T56KTEOYRZP/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":"2026-05-27T10:52:33Z","links":{"resolver":"https://pith.science/pith/Z4SCVC4UDJLCWY6T56KTEOYRZP","bundle":"https://pith.science/pith/Z4SCVC4UDJLCWY6T56KTEOYRZP/bundle.json","state":"https://pith.science/pith/Z4SCVC4UDJLCWY6T56KTEOYRZP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z4SCVC4UDJLCWY6T56KTEOYRZP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Z4SCVC4UDJLCWY6T56KTEOYRZP","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":"d4c9a0580722038e9c840673926bf6325127823754da36bc823450b3ee3f1350","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-08-27T05:18:44Z","title_canon_sha256":"2944892d79079e6143504660ed30645a82ae833e908fa73e2037db683dee42ec"},"schema_version":"1.0","source":{"id":"2208.12929","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2208.12929","created_at":"2026-05-18T03:09:47Z"},{"alias_kind":"arxiv_version","alias_value":"2208.12929v1","created_at":"2026-05-18T03:09:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2208.12929","created_at":"2026-05-18T03:09:47Z"},{"alias_kind":"pith_short_12","alias_value":"Z4SCVC4UDJLC","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_16","alias_value":"Z4SCVC4UDJLCWY6T","created_at":"2026-05-18T12:33:33Z"},{"alias_kind":"pith_short_8","alias_value":"Z4SCVC4U","created_at":"2026-05-18T12:33:33Z"}],"graph_snapshots":[{"event_id":"sha256:87e69d2bad26e846be5ba03f93144aef958502d0c058fad2cb8ef434a739273e","target":"graph","created_at":"2026-05-18T03:09:47Z","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":"Missing data are often dealt with multiple imputation. A crucial part of the multiple imputation process is selecting sensible models to generate plausible values for incomplete data. A method based on posterior predictive checking is proposed to diagnose imputation models based on posterior predictive checking. To assess the congeniality of imputation models, the proposed diagnostic method compares the observed data with their replicates generated under corresponding posterior predictive distributions. If the imputation model is congenial with the substantive model, the observed data are expe","authors_text":"Gerko Vink, Mingyang Cai, Stef van Buuren","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-08-27T05:18:44Z","title":"Graphical and numerical diagnostic tools to assess multiple imputation models by posterior predictive checking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2208.12929","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":"sha256:44d87ad750bde452148d4f855b740babbc4b53b48a5bf56fcfd718e253c61bd5","target":"record","created_at":"2026-05-18T03:09:47Z","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":"d4c9a0580722038e9c840673926bf6325127823754da36bc823450b3ee3f1350","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2022-08-27T05:18:44Z","title_canon_sha256":"2944892d79079e6143504660ed30645a82ae833e908fa73e2037db683dee42ec"},"schema_version":"1.0","source":{"id":"2208.12929","kind":"arxiv","version":1}},"canonical_sha256":"cf242a8b941a562b63d3ef95323b11cbefe8e58de167531ebf7ee45ad05adca2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf242a8b941a562b63d3ef95323b11cbefe8e58de167531ebf7ee45ad05adca2","first_computed_at":"2026-05-18T03:09:47.480429Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:47.480429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O/HQ7ymlfBz07W6MBYpz2haEpmzYW38s6AWLbVgGfVBtO2g5o0VJV7Ng1CJ6LEGfAcMXlegurkroInw6gJ2DCw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:47.481291Z","signed_message":"canonical_sha256_bytes"},"source_id":"2208.12929","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44d87ad750bde452148d4f855b740babbc4b53b48a5bf56fcfd718e253c61bd5","sha256:87e69d2bad26e846be5ba03f93144aef958502d0c058fad2cb8ef434a739273e"],"state_sha256":"b5a3928ad3688ae09aa8613fda78e211726f2a6b86122d667ba296e1f54943a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9cWr+DInQXQTPZzkmaSQWdpTA+dLkoWFwBYvcwJjOtQsPFiiFOJfzE8S2s1kIhLMmAmm2HQ62lgjGxIOzbhHDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T10:52:33.935081Z","bundle_sha256":"4419b4d22fc94cbeb882494081b3b8d4a37a28787c8eab02179b65b1effc5568"}}