{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:XBK7FZW74JLUDGA7JDHQEFM4ZS","short_pith_number":"pith:XBK7FZW7","schema_version":"1.0","canonical_sha256":"b855f2e6dfe25741981f48cf02159ccc82112567d71e3e38362238414365e7a3","source":{"kind":"arxiv","id":"1309.0423","version":2},"attestation_state":"computed","paper":{"title":"Nonparametric inference in hidden Markov models using P-splines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Alexander Sohn, Roland Langrock, Stacy DeRuiter, Thomas Kneib","submitted_at":"2013-09-02T14:32:31Z","abstract_excerpt":"Hidden Markov models (HMMs) are flexible time series models in which the distributions of the observations depend on unobserved serially correlated states. The state-dependent distributions in HMMs are usually taken from some class of parametrically specified distributions. The choice of this class can be difficult, and an unfortunate choice can have serious consequences for example on state estimates, on forecasts and generally on the resulting model complexity and interpretation, in particular with respect to the number of states. We develop a novel approach for estimating the state-dependen"},"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":"1309.0423","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-09-02T14:32:31Z","cross_cats_sorted":[],"title_canon_sha256":"c04806015cccb00435b6f4c0d681b155a9ec48d6044630d0a370876b42f178d8","abstract_canon_sha256":"4473e82b802ec613d99b959224ffc38815c6bf326836d52ef482e2fbc49f1494"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:49:31.751797Z","signature_b64":"b4+j1bcguHE/A1UoAA2UqjmSOx8locK+/XkE2A/e9JjY/3W8jv1e815q2kZyVvzFAI0samfQYWhF6TdkAVBWCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b855f2e6dfe25741981f48cf02159ccc82112567d71e3e38362238414365e7a3","last_reissued_at":"2026-05-18T02:49:31.751359Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:49:31.751359Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric inference in hidden Markov models using P-splines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Alexander Sohn, Roland Langrock, Stacy DeRuiter, Thomas Kneib","submitted_at":"2013-09-02T14:32:31Z","abstract_excerpt":"Hidden Markov models (HMMs) are flexible time series models in which the distributions of the observations depend on unobserved serially correlated states. The state-dependent distributions in HMMs are usually taken from some class of parametrically specified distributions. The choice of this class can be difficult, and an unfortunate choice can have serious consequences for example on state estimates, on forecasts and generally on the resulting model complexity and interpretation, in particular with respect to the number of states. We develop a novel approach for estimating the state-dependen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.0423","kind":"arxiv","version":2},"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":"1309.0423","created_at":"2026-05-18T02:49:31.751436+00:00"},{"alias_kind":"arxiv_version","alias_value":"1309.0423v2","created_at":"2026-05-18T02:49:31.751436+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.0423","created_at":"2026-05-18T02:49:31.751436+00:00"},{"alias_kind":"pith_short_12","alias_value":"XBK7FZW74JLU","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_16","alias_value":"XBK7FZW74JLUDGA7","created_at":"2026-05-18T12:28:06.772260+00:00"},{"alias_kind":"pith_short_8","alias_value":"XBK7FZW7","created_at":"2026-05-18T12:28:06.772260+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/XBK7FZW74JLUDGA7JDHQEFM4ZS","json":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS.json","graph_json":"https://pith.science/api/pith-number/XBK7FZW74JLUDGA7JDHQEFM4ZS/graph.json","events_json":"https://pith.science/api/pith-number/XBK7FZW74JLUDGA7JDHQEFM4ZS/events.json","paper":"https://pith.science/paper/XBK7FZW7"},"agent_actions":{"view_html":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS","download_json":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS.json","view_paper":"https://pith.science/paper/XBK7FZW7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1309.0423&json=true","fetch_graph":"https://pith.science/api/pith-number/XBK7FZW74JLUDGA7JDHQEFM4ZS/graph.json","fetch_events":"https://pith.science/api/pith-number/XBK7FZW74JLUDGA7JDHQEFM4ZS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS/action/storage_attestation","attest_author":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS/action/author_attestation","sign_citation":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS/action/citation_signature","submit_replication":"https://pith.science/pith/XBK7FZW74JLUDGA7JDHQEFM4ZS/action/replication_record"}},"created_at":"2026-05-18T02:49:31.751436+00:00","updated_at":"2026-05-18T02:49:31.751436+00:00"}