{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YLCTAIOOSBUZLP623XE2OI2QAN","short_pith_number":"pith:YLCTAIOO","schema_version":"1.0","canonical_sha256":"c2c53021ce906995bfdaddc9a72350035a535bca0432b45a695f9f81c60404a0","source":{"kind":"arxiv","id":"2608.13851","version":1},"attestation_state":"computed","paper":{"title":"Scalable likelihood-based inference for limited dependent variable models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"David T. Frazier, Didier Nibbering, Ruben Loaiza-Maya","submitted_at":"2026-08-14T00:49:24Z","abstract_excerpt":"Limited dependent variable models are central to empirical economics, but likelihood-based inference is infeasible when likelihoods involve high-dimensional integration over latent variables. This paper proposes Stochastically Estimated Gradient Ascent (SEGA), a scalable estimation approach for limited dependent variable models. Using Fisher's identity, SEGA replaces the intractable likelihood score with an unbiased augmented-data score evaluated at a single conditional draw of the latent variables, and embeds this score in a stochastic gradient ascent algorithm. With sufficiently many iterati"},"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":"2608.13851","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"econ.EM","submitted_at":"2026-08-14T00:49:24Z","cross_cats_sorted":[],"title_canon_sha256":"f55f7c78a6ce86bff50bad08d092a735a9421e23379b93f2263dacaa589dea8e","abstract_canon_sha256":"117f94062e5499c89c5433f5de47cbc998ec0ee290e038bef6bfd6c691066aca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-17T01:11:18.253115Z","signature_b64":"jV6+D0hhoAROEeGoT5bozjzzrW3o79orY/uBG+oUy7wW1A8r6fN+PBOAbdMy9tkdlcQYvFtCHqk4aEJ+ehwhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2c53021ce906995bfdaddc9a72350035a535bca0432b45a695f9f81c60404a0","last_reissued_at":"2026-08-17T01:11:18.250791Z","signature_status":"signed_v1","first_computed_at":"2026-08-17T01:11:18.250791Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable likelihood-based inference for limited dependent variable models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"econ.EM","authors_text":"David T. Frazier, Didier Nibbering, Ruben Loaiza-Maya","submitted_at":"2026-08-14T00:49:24Z","abstract_excerpt":"Limited dependent variable models are central to empirical economics, but likelihood-based inference is infeasible when likelihoods involve high-dimensional integration over latent variables. This paper proposes Stochastically Estimated Gradient Ascent (SEGA), a scalable estimation approach for limited dependent variable models. Using Fisher's identity, SEGA replaces the intractable likelihood score with an unbiased augmented-data score evaluated at a single conditional draw of the latent variables, and embeds this score in a stochastic gradient ascent algorithm. With sufficiently many iterati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.13851","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/2608.13851/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":"2608.13851","created_at":"2026-08-17T01:11:18.251500+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.13851v1","created_at":"2026-08-17T01:11:18.251500+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.13851","created_at":"2026-08-17T01:11:18.251500+00:00"},{"alias_kind":"pith_short_12","alias_value":"YLCTAIOOSBUZ","created_at":"2026-08-17T01:11:18.251500+00:00"},{"alias_kind":"pith_short_16","alias_value":"YLCTAIOOSBUZLP62","created_at":"2026-08-17T01:11:18.251500+00:00"},{"alias_kind":"pith_short_8","alias_value":"YLCTAIOO","created_at":"2026-08-17T01:11:18.251500+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/YLCTAIOOSBUZLP623XE2OI2QAN","json":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN.json","graph_json":"https://pith.science/api/pith-number/YLCTAIOOSBUZLP623XE2OI2QAN/graph.json","events_json":"https://pith.science/api/pith-number/YLCTAIOOSBUZLP623XE2OI2QAN/events.json","paper":"https://pith.science/paper/YLCTAIOO"},"agent_actions":{"view_html":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN","download_json":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN.json","view_paper":"https://pith.science/paper/YLCTAIOO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.13851&json=true","fetch_graph":"https://pith.science/api/pith-number/YLCTAIOOSBUZLP623XE2OI2QAN/graph.json","fetch_events":"https://pith.science/api/pith-number/YLCTAIOOSBUZLP623XE2OI2QAN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN/action/storage_attestation","attest_author":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN/action/author_attestation","sign_citation":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN/action/citation_signature","submit_replication":"https://pith.science/pith/YLCTAIOOSBUZLP623XE2OI2QAN/action/replication_record"}},"created_at":"2026-08-17T01:11:18.251500+00:00","updated_at":"2026-08-17T01:11:18.251500+00:00"}