{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AN3HHOGDJZSE62FJV5ZT2UPYUC","short_pith_number":"pith:AN3HHOGD","schema_version":"1.0","canonical_sha256":"037673b8c34e644f68a9af733d51f8a0b7d9ad0ef56f250d11dc3337e0de275d","source":{"kind":"arxiv","id":"2606.27046","version":1},"attestation_state":"computed","paper":{"title":"Conditional Leibniz Derivative Estimation with an Application to American Call Min-Options","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["q-fin.MF"],"primary_cat":"stat.ME","authors_text":"Michael C. Fu, Pierre L'Ecuyer, Xingyu Ren","submitted_at":"2026-06-25T13:52:03Z","abstract_excerpt":"Leibniz derivative estimation is a Monte Carlo technique for estimating derivatives of a discontinuous sample performance in stochastic models with respect to parameters of interest. By combining the push-out likelihood ratio (LR) method with Leibniz integral rules, it generalizes a broad class of existing LR-based derivative estimators. However, as an LR-based method, its variance is often higher than that of perturbation analysis-based methods and may grow linearly with the dimension of the stochastic input whose distribution depends on the parameter. In this paper, we propose a recursive co"},"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":"2606.27046","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2026-06-25T13:52:03Z","cross_cats_sorted":["q-fin.MF"],"title_canon_sha256":"559f19e776509552ddf7c2bb05d72933657b94933bf2d9270b34cb131d75ae22","abstract_canon_sha256":"098f4dbb9b294340fd2f6dfc8957e089c49c06a5d95cb791bbd67c40ed9f778f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:07.980465Z","signature_b64":"Ir0047QqC+1FZ554yEtgjiC4+fm9EXiTZ7RDHZqdZgATd7xTKzXgKu9l+qqFgIpscDKzuK7hRTq+M7AQyISkDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"037673b8c34e644f68a9af733d51f8a0b7d9ad0ef56f250d11dc3337e0de275d","last_reissued_at":"2026-06-26T01:16:07.980031Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:07.980031Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional Leibniz Derivative Estimation with an Application to American Call Min-Options","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["q-fin.MF"],"primary_cat":"stat.ME","authors_text":"Michael C. Fu, Pierre L'Ecuyer, Xingyu Ren","submitted_at":"2026-06-25T13:52:03Z","abstract_excerpt":"Leibniz derivative estimation is a Monte Carlo technique for estimating derivatives of a discontinuous sample performance in stochastic models with respect to parameters of interest. By combining the push-out likelihood ratio (LR) method with Leibniz integral rules, it generalizes a broad class of existing LR-based derivative estimators. However, as an LR-based method, its variance is often higher than that of perturbation analysis-based methods and may grow linearly with the dimension of the stochastic input whose distribution depends on the parameter. In this paper, we propose a recursive co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27046","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.27046/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2606.27046","created_at":"2026-06-26T01:16:07.980087+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.27046v1","created_at":"2026-06-26T01:16:07.980087+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27046","created_at":"2026-06-26T01:16:07.980087+00:00"},{"alias_kind":"pith_short_12","alias_value":"AN3HHOGDJZSE","created_at":"2026-06-26T01:16:07.980087+00:00"},{"alias_kind":"pith_short_16","alias_value":"AN3HHOGDJZSE62FJ","created_at":"2026-06-26T01:16:07.980087+00:00"},{"alias_kind":"pith_short_8","alias_value":"AN3HHOGD","created_at":"2026-06-26T01:16:07.980087+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/AN3HHOGDJZSE62FJV5ZT2UPYUC","json":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC.json","graph_json":"https://pith.science/api/pith-number/AN3HHOGDJZSE62FJV5ZT2UPYUC/graph.json","events_json":"https://pith.science/api/pith-number/AN3HHOGDJZSE62FJV5ZT2UPYUC/events.json","paper":"https://pith.science/paper/AN3HHOGD"},"agent_actions":{"view_html":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC","download_json":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC.json","view_paper":"https://pith.science/paper/AN3HHOGD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.27046&json=true","fetch_graph":"https://pith.science/api/pith-number/AN3HHOGDJZSE62FJV5ZT2UPYUC/graph.json","fetch_events":"https://pith.science/api/pith-number/AN3HHOGDJZSE62FJV5ZT2UPYUC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC/action/storage_attestation","attest_author":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC/action/author_attestation","sign_citation":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC/action/citation_signature","submit_replication":"https://pith.science/pith/AN3HHOGDJZSE62FJV5ZT2UPYUC/action/replication_record"}},"created_at":"2026-06-26T01:16:07.980087+00:00","updated_at":"2026-06-26T01:16:07.980087+00:00"}