{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CMZ2BLALNEAOPUEFPFGRFN4XDO","short_pith_number":"pith:CMZ2BLAL","schema_version":"1.0","canonical_sha256":"1333a0ac0b6900e7d085794d12b7971b9916f683968c0faefc217a211a2f1c88","source":{"kind":"arxiv","id":"1901.03269","version":1},"attestation_state":"computed","paper":{"title":"Smoothing Spline Semiparametric Density Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Anna Liu, Jiahui Yu, Jian Shi, Yuedong Wang","submitted_at":"2019-01-10T16:56:09Z","abstract_excerpt":"Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this paper, we consider a unified framework based on the reproducing kernel Hilbert space for modeling, estimation, computation and theory. We propose gene"},"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":"1901.03269","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-01-10T16:56:09Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"8a1350524f4d4113362dc5afc6f21a6c012517ae3896b4263c61e3a113586656","abstract_canon_sha256":"0227b444546e4a34de71bbcbe9f774657c678ecb44e820d476da5ba05319da48"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:34.911880Z","signature_b64":"0/7Rw5ztU2Pe7EOgMaMSoTJLg7tKI5kIb3N71z+2i28WJm/LMFgOKnDSvdliWd4b/aP+Vi9CLpki1FjrXYOIDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1333a0ac0b6900e7d085794d12b7971b9916f683968c0faefc217a211a2f1c88","last_reissued_at":"2026-05-17T23:56:34.911353Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:34.911353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Smoothing Spline Semiparametric Density Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Anna Liu, Jiahui Yu, Jian Shi, Yuedong Wang","submitted_at":"2019-01-10T16:56:09Z","abstract_excerpt":"Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this paper, we consider a unified framework based on the reproducing kernel Hilbert space for modeling, estimation, computation and theory. We propose gene"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.03269","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":"1901.03269","created_at":"2026-05-17T23:56:34.911446+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.03269v1","created_at":"2026-05-17T23:56:34.911446+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.03269","created_at":"2026-05-17T23:56:34.911446+00:00"},{"alias_kind":"pith_short_12","alias_value":"CMZ2BLALNEAO","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"CMZ2BLALNEAOPUEF","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"CMZ2BLAL","created_at":"2026-05-18T12:33:15.570797+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/CMZ2BLALNEAOPUEFPFGRFN4XDO","json":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO.json","graph_json":"https://pith.science/api/pith-number/CMZ2BLALNEAOPUEFPFGRFN4XDO/graph.json","events_json":"https://pith.science/api/pith-number/CMZ2BLALNEAOPUEFPFGRFN4XDO/events.json","paper":"https://pith.science/paper/CMZ2BLAL"},"agent_actions":{"view_html":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO","download_json":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO.json","view_paper":"https://pith.science/paper/CMZ2BLAL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.03269&json=true","fetch_graph":"https://pith.science/api/pith-number/CMZ2BLALNEAOPUEFPFGRFN4XDO/graph.json","fetch_events":"https://pith.science/api/pith-number/CMZ2BLALNEAOPUEFPFGRFN4XDO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO/action/storage_attestation","attest_author":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO/action/author_attestation","sign_citation":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO/action/citation_signature","submit_replication":"https://pith.science/pith/CMZ2BLALNEAOPUEFPFGRFN4XDO/action/replication_record"}},"created_at":"2026-05-17T23:56:34.911446+00:00","updated_at":"2026-05-17T23:56:34.911446+00:00"}