{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QWOIWJ3NZMNMEJAS6DRNZKVIGE","short_pith_number":"pith:QWOIWJ3N","schema_version":"1.0","canonical_sha256":"859c8b276dcb1ac22412f0e2dcaaa83135d439c81206ae14605b0197eab26601","source":{"kind":"arxiv","id":"2504.19089","version":1},"attestation_state":"computed","paper":{"title":"Semiparametric M-estimation with overparameterized neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Fang Yao, Shunxing Yan, Ziyuan Chen","submitted_at":"2025-04-27T03:09:20Z","abstract_excerpt":"We focus on semiparametric regression that has played a central role in statistics, and exploit the powerful learning ability of deep neural networks (DNNs) while enabling statistical inference on parameters of interest that offers interpretability. Despite the success of classical semiparametric method/theory, establishing the $\\sqrt{n}$-consistency and asymptotic normality of the finite-dimensional parameter estimator in this context remains challenging, mainly due to nonlinearity and potential tangent space degeneration in DNNs. In this work, we introduce a foundational framework for semipa"},"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":"2504.19089","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2025-04-27T03:09:20Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"8c73a19b3793029eb66cab18dab915b74d7ed885d9965a538739bf33a2671e58","abstract_canon_sha256":"82af5e8c2a39c05155d176c375bbe9583fcf03c2a25300499ce961e60d1ed561"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:54:51.850291Z","signature_b64":"tv//aUEQjCC3g0zPVD582ksc3GBEyTfRvGDMUydqUi7BrGOG0qHluScDQTmuzHhX3PmklqNb7KZgvNP2yiwpCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"859c8b276dcb1ac22412f0e2dcaaa83135d439c81206ae14605b0197eab26601","last_reissued_at":"2026-07-05T10:54:51.849838Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:54:51.849838Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Semiparametric M-estimation with overparameterized neural networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Fang Yao, Shunxing Yan, Ziyuan Chen","submitted_at":"2025-04-27T03:09:20Z","abstract_excerpt":"We focus on semiparametric regression that has played a central role in statistics, and exploit the powerful learning ability of deep neural networks (DNNs) while enabling statistical inference on parameters of interest that offers interpretability. Despite the success of classical semiparametric method/theory, establishing the $\\sqrt{n}$-consistency and asymptotic normality of the finite-dimensional parameter estimator in this context remains challenging, mainly due to nonlinearity and potential tangent space degeneration in DNNs. In this work, we introduce a foundational framework for semipa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.19089","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/2504.19089/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":"2504.19089","created_at":"2026-07-05T10:54:51.849896+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.19089v1","created_at":"2026-07-05T10:54:51.849896+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.19089","created_at":"2026-07-05T10:54:51.849896+00:00"},{"alias_kind":"pith_short_12","alias_value":"QWOIWJ3NZMNM","created_at":"2026-07-05T10:54:51.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"QWOIWJ3NZMNMEJAS","created_at":"2026-07-05T10:54:51.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"QWOIWJ3N","created_at":"2026-07-05T10:54:51.849896+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01257","citing_title":"Statistical Inference on Gradient Flows","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE","json":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE.json","graph_json":"https://pith.science/api/pith-number/QWOIWJ3NZMNMEJAS6DRNZKVIGE/graph.json","events_json":"https://pith.science/api/pith-number/QWOIWJ3NZMNMEJAS6DRNZKVIGE/events.json","paper":"https://pith.science/paper/QWOIWJ3N"},"agent_actions":{"view_html":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE","download_json":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE.json","view_paper":"https://pith.science/paper/QWOIWJ3N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.19089&json=true","fetch_graph":"https://pith.science/api/pith-number/QWOIWJ3NZMNMEJAS6DRNZKVIGE/graph.json","fetch_events":"https://pith.science/api/pith-number/QWOIWJ3NZMNMEJAS6DRNZKVIGE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE/action/storage_attestation","attest_author":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE/action/author_attestation","sign_citation":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE/action/citation_signature","submit_replication":"https://pith.science/pith/QWOIWJ3NZMNMEJAS6DRNZKVIGE/action/replication_record"}},"created_at":"2026-07-05T10:54:51.849896+00:00","updated_at":"2026-07-05T10:54:51.849896+00:00"}