{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:6IX5DIM2PIAOLAGIL5JK7UNESR","short_pith_number":"pith:6IX5DIM2","schema_version":"1.0","canonical_sha256":"f22fd1a19a7a00e580c85f52afd1a4945f04c53266f6da3b256bd69d117d31d0","source":{"kind":"arxiv","id":"1601.04331","version":3},"attestation_state":"computed","paper":{"title":"A Bayesian Nonparametric Markovian Model for Nonstationary Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Athanasios Kottas, Maria DeYoreo","submitted_at":"2016-01-17T19:37:58Z","abstract_excerpt":"Stationary time series models built from parametric distributions are, in general, limited in scope due to the assumptions imposed on the residual distribution and autoregression relationship. We present a modeling approach for univariate time series data, which makes no assumptions of stationarity, and can accommodate complex dynamics and capture nonstandard distributions. The model for the transition density arises from the conditional distribution implied by a Bayesian nonparametric mixture of bivariate normals. This implies a flexible autoregressive form for the conditional transition dens"},"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":"1601.04331","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-01-17T19:37:58Z","cross_cats_sorted":[],"title_canon_sha256":"b2f454484151eb18eeacd5431461338a971c981456ed5ef9d93fc1dbf8666a4c","abstract_canon_sha256":"3697a421ab102a4414b94e510fb869e08e75cb808e3802e6021c542ea594feb7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:15:51.354094Z","signature_b64":"laQi8tIwJ1R5t2SXvpuKAbVV7/a90ZRxN3JuGwMIYWPSHGYeF6og4tjU6Tdkkx5/ROdOnXVgwkkfdimDHG0TBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f22fd1a19a7a00e580c85f52afd1a4945f04c53266f6da3b256bd69d117d31d0","last_reissued_at":"2026-05-18T01:15:51.353632Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:15:51.353632Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bayesian Nonparametric Markovian Model for Nonstationary Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Athanasios Kottas, Maria DeYoreo","submitted_at":"2016-01-17T19:37:58Z","abstract_excerpt":"Stationary time series models built from parametric distributions are, in general, limited in scope due to the assumptions imposed on the residual distribution and autoregression relationship. We present a modeling approach for univariate time series data, which makes no assumptions of stationarity, and can accommodate complex dynamics and capture nonstandard distributions. The model for the transition density arises from the conditional distribution implied by a Bayesian nonparametric mixture of bivariate normals. This implies a flexible autoregressive form for the conditional transition dens"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.04331","kind":"arxiv","version":3},"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":"1601.04331","created_at":"2026-05-18T01:15:51.353699+00:00"},{"alias_kind":"arxiv_version","alias_value":"1601.04331v3","created_at":"2026-05-18T01:15:51.353699+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.04331","created_at":"2026-05-18T01:15:51.353699+00:00"},{"alias_kind":"pith_short_12","alias_value":"6IX5DIM2PIAO","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_16","alias_value":"6IX5DIM2PIAOLAGI","created_at":"2026-05-18T12:30:01.593930+00:00"},{"alias_kind":"pith_short_8","alias_value":"6IX5DIM2","created_at":"2026-05-18T12:30:01.593930+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/6IX5DIM2PIAOLAGIL5JK7UNESR","json":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR.json","graph_json":"https://pith.science/api/pith-number/6IX5DIM2PIAOLAGIL5JK7UNESR/graph.json","events_json":"https://pith.science/api/pith-number/6IX5DIM2PIAOLAGIL5JK7UNESR/events.json","paper":"https://pith.science/paper/6IX5DIM2"},"agent_actions":{"view_html":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR","download_json":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR.json","view_paper":"https://pith.science/paper/6IX5DIM2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1601.04331&json=true","fetch_graph":"https://pith.science/api/pith-number/6IX5DIM2PIAOLAGIL5JK7UNESR/graph.json","fetch_events":"https://pith.science/api/pith-number/6IX5DIM2PIAOLAGIL5JK7UNESR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR/action/storage_attestation","attest_author":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR/action/author_attestation","sign_citation":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR/action/citation_signature","submit_replication":"https://pith.science/pith/6IX5DIM2PIAOLAGIL5JK7UNESR/action/replication_record"}},"created_at":"2026-05-18T01:15:51.353699+00:00","updated_at":"2026-05-18T01:15:51.353699+00:00"}