{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UBKP3P6YTAYPGHKLPMIPY7U5FE","short_pith_number":"pith:UBKP3P6Y","schema_version":"1.0","canonical_sha256":"a054fdbfd89830f31d4b7b10fc7e9d2922942fd731ca2695a890a7b5ae27d727","source":{"kind":"arxiv","id":"2405.12386","version":1},"attestation_state":"computed","paper":{"title":"Particle swarm optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.CO"],"primary_cat":"stat.ML","authors_text":"Junhyung Park, Sisi Shao, Weng Kee Wong","submitted_at":"2024-05-20T21:42:42Z","abstract_excerpt":"General purpose optimization routines such as nlminb, optim (R) or nlmixed (SAS) are frequently used to estimate model parameters in nonstandard distributions. This paper presents Particle Swarm Optimization (PSO), as an alternative to many of the current algorithms used in statistics. We find that PSO can not only reproduce the same results as the above routines, it can also produce results that are more optimal or when others cannot converge. In the latter case, it can also identify the source of the problem or problems. We highlight advantages of using PSO using four examples, where: (1) so"},"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":"2405.12386","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2024-05-20T21:42:42Z","cross_cats_sorted":["cs.LG","stat.AP","stat.CO"],"title_canon_sha256":"dfd8c09234beb2fd23cfe045e66a8d1cc5eba6ba3eb93ab6fdb1fa5d91987eea","abstract_canon_sha256":"9ab71489a19b3e15ea6ab496825d40429d97a836ff9adf081836cc2da7920795"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:21:19.185180Z","signature_b64":"4FZRmevopR/sRKydoAP4Gi+Gd7nqqiNkcXIiK1vXbMPtjJ4tMckX1BeY0FtAv0EqUv/YqdN1Y6fIboWNF1rXDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a054fdbfd89830f31d4b7b10fc7e9d2922942fd731ca2695a890a7b5ae27d727","last_reissued_at":"2026-07-05T08:21:19.184697Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:21:19.184697Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Particle swarm optimization with Applications to Maximum Likelihood Estimation and Penalized Negative Binomial Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.CO"],"primary_cat":"stat.ML","authors_text":"Junhyung Park, Sisi Shao, Weng Kee Wong","submitted_at":"2024-05-20T21:42:42Z","abstract_excerpt":"General purpose optimization routines such as nlminb, optim (R) or nlmixed (SAS) are frequently used to estimate model parameters in nonstandard distributions. This paper presents Particle Swarm Optimization (PSO), as an alternative to many of the current algorithms used in statistics. We find that PSO can not only reproduce the same results as the above routines, it can also produce results that are more optimal or when others cannot converge. In the latter case, it can also identify the source of the problem or problems. We highlight advantages of using PSO using four examples, where: (1) so"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.12386","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/2405.12386/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":"2405.12386","created_at":"2026-07-05T08:21:19.184761+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.12386v1","created_at":"2026-07-05T08:21:19.184761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.12386","created_at":"2026-07-05T08:21:19.184761+00:00"},{"alias_kind":"pith_short_12","alias_value":"UBKP3P6YTAYP","created_at":"2026-07-05T08:21:19.184761+00:00"},{"alias_kind":"pith_short_16","alias_value":"UBKP3P6YTAYPGHKL","created_at":"2026-07-05T08:21:19.184761+00:00"},{"alias_kind":"pith_short_8","alias_value":"UBKP3P6Y","created_at":"2026-07-05T08:21:19.184761+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/UBKP3P6YTAYPGHKLPMIPY7U5FE","json":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE.json","graph_json":"https://pith.science/api/pith-number/UBKP3P6YTAYPGHKLPMIPY7U5FE/graph.json","events_json":"https://pith.science/api/pith-number/UBKP3P6YTAYPGHKLPMIPY7U5FE/events.json","paper":"https://pith.science/paper/UBKP3P6Y"},"agent_actions":{"view_html":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE","download_json":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE.json","view_paper":"https://pith.science/paper/UBKP3P6Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.12386&json=true","fetch_graph":"https://pith.science/api/pith-number/UBKP3P6YTAYPGHKLPMIPY7U5FE/graph.json","fetch_events":"https://pith.science/api/pith-number/UBKP3P6YTAYPGHKLPMIPY7U5FE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE/action/storage_attestation","attest_author":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE/action/author_attestation","sign_citation":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE/action/citation_signature","submit_replication":"https://pith.science/pith/UBKP3P6YTAYPGHKLPMIPY7U5FE/action/replication_record"}},"created_at":"2026-07-05T08:21:19.184761+00:00","updated_at":"2026-07-05T08:21:19.184761+00:00"}