{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:6ZERYH5HDDT6QRKLPMCPGIRPFB","short_pith_number":"pith:6ZERYH5H","schema_version":"1.0","canonical_sha256":"f6491c1fa718e7e8454b7b04f3222f2850cd406222c94916cdde8dd396c1e669","source":{"kind":"arxiv","id":"1303.4121","version":1},"attestation_state":"computed","paper":{"title":"Probit transformation for kernel density estimation on the unit interval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Gery Geenens","submitted_at":"2013-03-17T23:42:28Z","abstract_excerpt":"Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well known boundary bias issues a conventional kernel density estimator would necessarily face in this situation. Transforming the variable of interest into a variable whose density has unconstrained support, estimating that density, and obtaining an estimate of the density of the original variable through back-transformation, seems a natural idea to easily get rid of the boundary problems. In practice, however, a simple and efficient implementation of this methodology is far"},"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":"1303.4121","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-03-17T23:42:28Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"f64a20d48a2426a241d3ea59bdd0c89f5cd761a9404d48ee09e6d96cd3783849","abstract_canon_sha256":"02a45fc9c9f88c773d7073d564ac386fedc74d1c67c11a2ec1eefbed4aaa73c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:30:39.722296Z","signature_b64":"0+ynPJsem9f/Zp9KPqjao/fqVoVI5qb3evaEzPU5/up9X3F4/uPuR3RUIOSPP8ePF8FXSflpR4z8O/amNfkvBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6491c1fa718e7e8454b7b04f3222f2850cd406222c94916cdde8dd396c1e669","last_reissued_at":"2026-05-18T03:30:39.721396Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:30:39.721396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probit transformation for kernel density estimation on the unit interval","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Gery Geenens","submitted_at":"2013-03-17T23:42:28Z","abstract_excerpt":"Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well known boundary bias issues a conventional kernel density estimator would necessarily face in this situation. Transforming the variable of interest into a variable whose density has unconstrained support, estimating that density, and obtaining an estimate of the density of the original variable through back-transformation, seems a natural idea to easily get rid of the boundary problems. In practice, however, a simple and efficient implementation of this methodology is far"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1303.4121","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":"1303.4121","created_at":"2026-05-18T03:30:39.721562+00:00"},{"alias_kind":"arxiv_version","alias_value":"1303.4121v1","created_at":"2026-05-18T03:30:39.721562+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1303.4121","created_at":"2026-05-18T03:30:39.721562+00:00"},{"alias_kind":"pith_short_12","alias_value":"6ZERYH5HDDT6","created_at":"2026-05-18T12:27:36.564083+00:00"},{"alias_kind":"pith_short_16","alias_value":"6ZERYH5HDDT6QRKL","created_at":"2026-05-18T12:27:36.564083+00:00"},{"alias_kind":"pith_short_8","alias_value":"6ZERYH5H","created_at":"2026-05-18T12:27:36.564083+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/6ZERYH5HDDT6QRKLPMCPGIRPFB","json":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB.json","graph_json":"https://pith.science/api/pith-number/6ZERYH5HDDT6QRKLPMCPGIRPFB/graph.json","events_json":"https://pith.science/api/pith-number/6ZERYH5HDDT6QRKLPMCPGIRPFB/events.json","paper":"https://pith.science/paper/6ZERYH5H"},"agent_actions":{"view_html":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB","download_json":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB.json","view_paper":"https://pith.science/paper/6ZERYH5H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1303.4121&json=true","fetch_graph":"https://pith.science/api/pith-number/6ZERYH5HDDT6QRKLPMCPGIRPFB/graph.json","fetch_events":"https://pith.science/api/pith-number/6ZERYH5HDDT6QRKLPMCPGIRPFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB/action/storage_attestation","attest_author":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB/action/author_attestation","sign_citation":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB/action/citation_signature","submit_replication":"https://pith.science/pith/6ZERYH5HDDT6QRKLPMCPGIRPFB/action/replication_record"}},"created_at":"2026-05-18T03:30:39.721562+00:00","updated_at":"2026-05-18T03:30:39.721562+00:00"}