{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ISPCDYP3ANKGRT2BKFXWQST5AA","short_pith_number":"pith:ISPCDYP3","schema_version":"1.0","canonical_sha256":"449e21e1fb035468cf41516f684a7d00038bb9211380a79b6da852697fbe6dc6","source":{"kind":"arxiv","id":"1812.03555","version":1},"attestation_state":"computed","paper":{"title":"Spatio-Temporal Models for Big Multinomial Data using the Conditional Multivariate Logit-Beta Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Christopher K. Wikle, Jonathan R. Bradley, Scott H. Holan","submitted_at":"2018-12-09T20:30:51Z","abstract_excerpt":"We introduce a Bayesian approach for analyzing high-dimensional multinomial data that are referenced over space and time. In particular, the proportions associated with multinomial data are assumed to have a logit link to a latent spatio-temporal mixed effects model. This strategy allows for covariances that are nonstationarity in both space and time, asymmetric, and parsimonious. We also introduce the use of the conditional multivariate logit-beta distribution into the dependent multinomial data setting, which leads to conjugate full-conditional distributions for use in a collapsed Gibbs samp"},"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":"1812.03555","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-12-09T20:30:51Z","cross_cats_sorted":[],"title_canon_sha256":"71dd8d07bc86b7e4c4bf1a0a3557fff21c72fe86422952a7a25b13824bac8f43","abstract_canon_sha256":"8536ca21a236676e473c7c5de2a6c3fa7c26d9802054074c81ebffc2b2572c9f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:46.033744Z","signature_b64":"Z0W7IFJbd/A7CFPtOgoTEa2h26mdSr6pmKd4GnS7hP8InI7dPRIKYFxqjmwpcPAGpJTBlPX+ULJPzHIU8kE6Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"449e21e1fb035468cf41516f684a7d00038bb9211380a79b6da852697fbe6dc6","last_reissued_at":"2026-05-17T23:58:46.033335Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:46.033335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spatio-Temporal Models for Big Multinomial Data using the Conditional Multivariate Logit-Beta Distribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Christopher K. Wikle, Jonathan R. Bradley, Scott H. Holan","submitted_at":"2018-12-09T20:30:51Z","abstract_excerpt":"We introduce a Bayesian approach for analyzing high-dimensional multinomial data that are referenced over space and time. In particular, the proportions associated with multinomial data are assumed to have a logit link to a latent spatio-temporal mixed effects model. This strategy allows for covariances that are nonstationarity in both space and time, asymmetric, and parsimonious. We also introduce the use of the conditional multivariate logit-beta distribution into the dependent multinomial data setting, which leads to conjugate full-conditional distributions for use in a collapsed Gibbs samp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.03555","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1812.03555","created_at":"2026-05-17T23:58:46.033402+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.03555v1","created_at":"2026-05-17T23:58:46.033402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.03555","created_at":"2026-05-17T23:58:46.033402+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISPCDYP3ANKG","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISPCDYP3ANKGRT2B","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISPCDYP3","created_at":"2026-05-18T12:32:31.084164+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/ISPCDYP3ANKGRT2BKFXWQST5AA","json":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA.json","graph_json":"https://pith.science/api/pith-number/ISPCDYP3ANKGRT2BKFXWQST5AA/graph.json","events_json":"https://pith.science/api/pith-number/ISPCDYP3ANKGRT2BKFXWQST5AA/events.json","paper":"https://pith.science/paper/ISPCDYP3"},"agent_actions":{"view_html":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA","download_json":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA.json","view_paper":"https://pith.science/paper/ISPCDYP3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.03555&json=true","fetch_graph":"https://pith.science/api/pith-number/ISPCDYP3ANKGRT2BKFXWQST5AA/graph.json","fetch_events":"https://pith.science/api/pith-number/ISPCDYP3ANKGRT2BKFXWQST5AA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA/action/storage_attestation","attest_author":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA/action/author_attestation","sign_citation":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA/action/citation_signature","submit_replication":"https://pith.science/pith/ISPCDYP3ANKGRT2BKFXWQST5AA/action/replication_record"}},"created_at":"2026-05-17T23:58:46.033402+00:00","updated_at":"2026-05-17T23:58:46.033402+00:00"}