{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:P2QLDJHM5VYGNKJQ6JE5B52JED","short_pith_number":"pith:P2QLDJHM","schema_version":"1.0","canonical_sha256":"7ea0b1a4eced7066a930f249d0f74920c4a02474a941919f735defc18bd79408","source":{"kind":"arxiv","id":"2408.06103","version":3},"attestation_state":"computed","paper":{"title":"Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["econ.EM","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Lin Liu, Rajarshi Mukherjee, Xingyu Chen","submitted_at":"2024-08-12T12:43:30Z","abstract_excerpt":"In this paper, we consider the estimation of regression coefficients and signal-to-noise (SNR) ratio in high-dimensional Generalized Linear Models (GLMs), and explore their implications in inferring popular estimands such as average treatment effects in high-dimensional observational studies. Under the ``proportional asymptotic'' regime and Gaussian covariates with known (population) covariance $\\Sigma$, we derive Consistent and Asymptotically Normal (CAN) estimators of our targets of inference through a Method-of-Moments type of estimators that bypasses estimation of high dimensional nuisance"},"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":"2408.06103","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-08-12T12:43:30Z","cross_cats_sorted":["econ.EM","stat.ME","stat.ML","stat.TH"],"title_canon_sha256":"6b7a252603f428584bafb3de529f3c1d3b305a2916512f2c41fe9878d69eb5ac","abstract_canon_sha256":"0b36185dc7a8af49bde5203a0e084200b0d764333349c7967ba559da62015c49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:59:01.694144Z","signature_b64":"s+WIDRDnmwRuDgy9yKzgvmEFWujo9LCw2lLgzWHsL0SnGEAwWRut9Yceihe5kSQHyhA+So+WMBexAmjDork9Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ea0b1a4eced7066a930f249d0f74920c4a02474a941919f735defc18bd79408","last_reissued_at":"2026-07-05T10:59:01.693551Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:59:01.693551Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Method-of-Moments Inference for GLMs and Doubly Robust Functionals under Proportional Asymptotics","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["econ.EM","stat.ME","stat.ML","stat.TH"],"primary_cat":"math.ST","authors_text":"Lin Liu, Rajarshi Mukherjee, Xingyu Chen","submitted_at":"2024-08-12T12:43:30Z","abstract_excerpt":"In this paper, we consider the estimation of regression coefficients and signal-to-noise (SNR) ratio in high-dimensional Generalized Linear Models (GLMs), and explore their implications in inferring popular estimands such as average treatment effects in high-dimensional observational studies. Under the ``proportional asymptotic'' regime and Gaussian covariates with known (population) covariance $\\Sigma$, we derive Consistent and Asymptotically Normal (CAN) estimators of our targets of inference through a Method-of-Moments type of estimators that bypasses estimation of high dimensional nuisance"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.06103","kind":"arxiv","version":3},"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/2408.06103/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":"2408.06103","created_at":"2026-07-05T10:59:01.693626+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.06103v3","created_at":"2026-07-05T10:59:01.693626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.06103","created_at":"2026-07-05T10:59:01.693626+00:00"},{"alias_kind":"pith_short_12","alias_value":"P2QLDJHM5VYG","created_at":"2026-07-05T10:59:01.693626+00:00"},{"alias_kind":"pith_short_16","alias_value":"P2QLDJHM5VYGNKJQ","created_at":"2026-07-05T10:59:01.693626+00:00"},{"alias_kind":"pith_short_8","alias_value":"P2QLDJHM","created_at":"2026-07-05T10:59:01.693626+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.01706","citing_title":"Higher-Order Debiased Estimators for General Treatment Models","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05076","citing_title":"High-Dimensional Statistics: Reflections on Progress and Open Problems","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17778","citing_title":"Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models","ref_index":35,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05076","citing_title":"High-Dimensional Statistics: Reflections on Progress and Open Problems","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED","json":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED.json","graph_json":"https://pith.science/api/pith-number/P2QLDJHM5VYGNKJQ6JE5B52JED/graph.json","events_json":"https://pith.science/api/pith-number/P2QLDJHM5VYGNKJQ6JE5B52JED/events.json","paper":"https://pith.science/paper/P2QLDJHM"},"agent_actions":{"view_html":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED","download_json":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED.json","view_paper":"https://pith.science/paper/P2QLDJHM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.06103&json=true","fetch_graph":"https://pith.science/api/pith-number/P2QLDJHM5VYGNKJQ6JE5B52JED/graph.json","fetch_events":"https://pith.science/api/pith-number/P2QLDJHM5VYGNKJQ6JE5B52JED/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED/action/timestamp_anchor","attest_storage":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED/action/storage_attestation","attest_author":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED/action/author_attestation","sign_citation":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED/action/citation_signature","submit_replication":"https://pith.science/pith/P2QLDJHM5VYGNKJQ6JE5B52JED/action/replication_record"}},"created_at":"2026-07-05T10:59:01.693626+00:00","updated_at":"2026-07-05T10:59:01.693626+00:00"}