{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:WV3FR5GPBT47NZCCG2IFTNQKT2","short_pith_number":"pith:WV3FR5GP","schema_version":"1.0","canonical_sha256":"b57658f4cf0cf9f6e442369059b60a9ea685d2d597864977da8609cd4309d07e","source":{"kind":"arxiv","id":"1901.05768","version":1},"attestation_state":"computed","paper":{"title":"A Multi-Level Simulation Optimization Approach for Quantile Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"math.OC","authors_text":"Songhao Wang, Szu Hui Ng, William Benjamin Haskell","submitted_at":"2019-01-17T12:45:58Z","abstract_excerpt":"Quantile is a popular performance measure for a stochastic system to evaluate its variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles of some loss distributions is typically of interest for decision makers. When the loss distribution is observed via simulations, evaluating and optimizing its quantile functions can be challenging, especially when the simulations are expensive, as it may cost a large number of simulation runs to obtain accurate quantile estimators. In this work, we propose a multi-level metamodel (co-kriging) based algorithm to optimi"},"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":"1901.05768","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2019-01-17T12:45:58Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"48cf8f761cc0af02e14c59bbc5e9e2c0e91ed4ea9ac82a26e8475ad7b8a64c60","abstract_canon_sha256":"28be1c3a9ec5da08ea7b7b077cc4409bdf8067230c73c103935ad5d48349b2e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:56:06.221456Z","signature_b64":"2GGF9x0rZgxOa/NRqOHqCYClv6m4Znq1u66PJwzAVUw1ZB5BNGhO14JJcYngDY2OR46Rr59S4Y6nu5i08A5vBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b57658f4cf0cf9f6e442369059b60a9ea685d2d597864977da8609cd4309d07e","last_reissued_at":"2026-05-17T23:56:06.220913Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:56:06.220913Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Multi-Level Simulation Optimization Approach for Quantile Functions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"math.OC","authors_text":"Songhao Wang, Szu Hui Ng, William Benjamin Haskell","submitted_at":"2019-01-17T12:45:58Z","abstract_excerpt":"Quantile is a popular performance measure for a stochastic system to evaluate its variability and risk. To reduce the risk, selecting the actions that minimize the tail quantiles of some loss distributions is typically of interest for decision makers. When the loss distribution is observed via simulations, evaluating and optimizing its quantile functions can be challenging, especially when the simulations are expensive, as it may cost a large number of simulation runs to obtain accurate quantile estimators. In this work, we propose a multi-level metamodel (co-kriging) based algorithm to optimi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.05768","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":"1901.05768","created_at":"2026-05-17T23:56:06.220976+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.05768v1","created_at":"2026-05-17T23:56:06.220976+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.05768","created_at":"2026-05-17T23:56:06.220976+00:00"},{"alias_kind":"pith_short_12","alias_value":"WV3FR5GPBT47","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"WV3FR5GPBT47NZCC","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"WV3FR5GP","created_at":"2026-05-18T12:33:33.725879+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/WV3FR5GPBT47NZCCG2IFTNQKT2","json":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2.json","graph_json":"https://pith.science/api/pith-number/WV3FR5GPBT47NZCCG2IFTNQKT2/graph.json","events_json":"https://pith.science/api/pith-number/WV3FR5GPBT47NZCCG2IFTNQKT2/events.json","paper":"https://pith.science/paper/WV3FR5GP"},"agent_actions":{"view_html":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2","download_json":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2.json","view_paper":"https://pith.science/paper/WV3FR5GP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.05768&json=true","fetch_graph":"https://pith.science/api/pith-number/WV3FR5GPBT47NZCCG2IFTNQKT2/graph.json","fetch_events":"https://pith.science/api/pith-number/WV3FR5GPBT47NZCCG2IFTNQKT2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2/action/storage_attestation","attest_author":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2/action/author_attestation","sign_citation":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2/action/citation_signature","submit_replication":"https://pith.science/pith/WV3FR5GPBT47NZCCG2IFTNQKT2/action/replication_record"}},"created_at":"2026-05-17T23:56:06.220976+00:00","updated_at":"2026-05-17T23:56:06.220976+00:00"}