{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BM6ZRXJNXZUBTC35FOBIGUOMD3","short_pith_number":"pith:BM6ZRXJN","schema_version":"1.0","canonical_sha256":"0b3d98dd2dbe68198b7d2b828351cc1ed2936765ea1d7eabffca746b6126612b","source":{"kind":"arxiv","id":"1912.10981","version":1},"attestation_state":"computed","paper":{"title":"Missing data analysis and imputation via latent Gaussian Markov random fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.CO","authors_text":"Marta Blangiardo, Michela Cameletti, Virgilio G\\'omez-Rubio","submitted_at":"2019-12-23T17:16:28Z","abstract_excerpt":"In this paper we recast the problem of missing values in the covariates of a regression model as a latent Gaussian Markov random field (GMRF) model in a fully Bayesian framework. Our proposed approach is based on the definition of the covariate imputation sub-model as a latent effect with a GMRF structure. We show how this formulation works for continuous covariates and provide some insight on how this could be extended to categorical covariates.\n  The resulting Bayesian hierarchical model naturally fits within the integrated nested Laplace approximation (INLA) framework, which we use for mode"},"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":"1912.10981","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2019-12-23T17:16:28Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"9033b78383ab374a6315962ef2da61405a1171a308849f29ca5ced465eff08c3","abstract_canon_sha256":"e2c2d36fe7bef77f1f9637436d09629d1ff1ed86ccf1eafa15a37a3046c0bfc7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:28:04.585220Z","signature_b64":"kB+OwR4mCMOZHyPmW9nGuxFS1eoY6Htyn2BG440s3++9/WznSrn0sGl94qcfSwcML2JZwrFQ+siBjenk1+1HDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b3d98dd2dbe68198b7d2b828351cc1ed2936765ea1d7eabffca746b6126612b","last_reissued_at":"2026-07-05T00:28:04.584730Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:28:04.584730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Missing data analysis and imputation via latent Gaussian Markov random fields","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.CO","authors_text":"Marta Blangiardo, Michela Cameletti, Virgilio G\\'omez-Rubio","submitted_at":"2019-12-23T17:16:28Z","abstract_excerpt":"In this paper we recast the problem of missing values in the covariates of a regression model as a latent Gaussian Markov random field (GMRF) model in a fully Bayesian framework. Our proposed approach is based on the definition of the covariate imputation sub-model as a latent effect with a GMRF structure. We show how this formulation works for continuous covariates and provide some insight on how this could be extended to categorical covariates.\n  The resulting Bayesian hierarchical model naturally fits within the integrated nested Laplace approximation (INLA) framework, which we use for mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.10981","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1912.10981/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"1912.10981","created_at":"2026-07-05T00:28:04.584794+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.10981v1","created_at":"2026-07-05T00:28:04.584794+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.10981","created_at":"2026-07-05T00:28:04.584794+00:00"},{"alias_kind":"pith_short_12","alias_value":"BM6ZRXJNXZUB","created_at":"2026-07-05T00:28:04.584794+00:00"},{"alias_kind":"pith_short_16","alias_value":"BM6ZRXJNXZUBTC35","created_at":"2026-07-05T00:28:04.584794+00:00"},{"alias_kind":"pith_short_8","alias_value":"BM6ZRXJN","created_at":"2026-07-05T00:28:04.584794+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26323","citing_title":"False Positives, False Negatives, and the Detection-Only Problem: A Hierarchical Model for Species Occurrence with Observation Error","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3","json":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3.json","graph_json":"https://pith.science/api/pith-number/BM6ZRXJNXZUBTC35FOBIGUOMD3/graph.json","events_json":"https://pith.science/api/pith-number/BM6ZRXJNXZUBTC35FOBIGUOMD3/events.json","paper":"https://pith.science/paper/BM6ZRXJN"},"agent_actions":{"view_html":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3","download_json":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3.json","view_paper":"https://pith.science/paper/BM6ZRXJN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.10981&json=true","fetch_graph":"https://pith.science/api/pith-number/BM6ZRXJNXZUBTC35FOBIGUOMD3/graph.json","fetch_events":"https://pith.science/api/pith-number/BM6ZRXJNXZUBTC35FOBIGUOMD3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3/action/storage_attestation","attest_author":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3/action/author_attestation","sign_citation":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3/action/citation_signature","submit_replication":"https://pith.science/pith/BM6ZRXJNXZUBTC35FOBIGUOMD3/action/replication_record"}},"created_at":"2026-07-05T00:28:04.584794+00:00","updated_at":"2026-07-05T00:28:04.584794+00:00"}