{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:CSVM6P4V2UMQTIPPKBJMQFHQ6M","short_pith_number":"pith:CSVM6P4V","schema_version":"1.0","canonical_sha256":"14aacf3f95d51909a1ef5052c814f0f31acc3530b2b6c6db0c2b6665f4827603","source":{"kind":"arxiv","id":"1703.03965","version":1},"attestation_state":"computed","paper":{"title":"Sparse Poisson Regression with Penalized Weighted Score Function","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Fang Xie, Jinzhu Jia, Lihu Xu","submitted_at":"2017-03-11T12:39:33Z","abstract_excerpt":"We proposed a new penalized method in this paper to solve sparse Poisson Regression problems. Being different from $\\ell_1$ penalized log-likelihood estimation, our new method can be viewed as penalized weighted score function method. We show that under mild conditions, our estimator is $\\ell_1$ consistent and the tuning parameter can be pre-specified, which shares the same good property of the square-root Lasso."},"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":"1703.03965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-03-11T12:39:33Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"33624fb3a1bae24c6717491097bafd349ef438bf3e7c1a75fb56b5e7b970c590","abstract_canon_sha256":"61b51f45a9cd7354a3682d81c6886e6dd1b062af758cc45796f0b68e762e9a83"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:52.557504Z","signature_b64":"+5Urgu5voxbOAZtJuebKh7SnIL/Asr5Xm92d7aDDIbK99p2QPx4ICLManrZaq+1QjN81dcFuqA2lwusz//RSDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14aacf3f95d51909a1ef5052c814f0f31acc3530b2b6c6db0c2b6665f4827603","last_reissued_at":"2026-05-18T00:48:52.556580Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:52.556580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sparse Poisson Regression with Penalized Weighted Score Function","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Fang Xie, Jinzhu Jia, Lihu Xu","submitted_at":"2017-03-11T12:39:33Z","abstract_excerpt":"We proposed a new penalized method in this paper to solve sparse Poisson Regression problems. Being different from $\\ell_1$ penalized log-likelihood estimation, our new method can be viewed as penalized weighted score function method. We show that under mild conditions, our estimator is $\\ell_1$ consistent and the tuning parameter can be pre-specified, which shares the same good property of the square-root Lasso."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03965","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":"1703.03965","created_at":"2026-05-18T00:48:52.556730+00:00"},{"alias_kind":"arxiv_version","alias_value":"1703.03965v1","created_at":"2026-05-18T00:48:52.556730+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03965","created_at":"2026-05-18T00:48:52.556730+00:00"},{"alias_kind":"pith_short_12","alias_value":"CSVM6P4V2UMQ","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_16","alias_value":"CSVM6P4V2UMQTIPP","created_at":"2026-05-18T12:31:10.602751+00:00"},{"alias_kind":"pith_short_8","alias_value":"CSVM6P4V","created_at":"2026-05-18T12:31:10.602751+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/CSVM6P4V2UMQTIPPKBJMQFHQ6M","json":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M.json","graph_json":"https://pith.science/api/pith-number/CSVM6P4V2UMQTIPPKBJMQFHQ6M/graph.json","events_json":"https://pith.science/api/pith-number/CSVM6P4V2UMQTIPPKBJMQFHQ6M/events.json","paper":"https://pith.science/paper/CSVM6P4V"},"agent_actions":{"view_html":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M","download_json":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M.json","view_paper":"https://pith.science/paper/CSVM6P4V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1703.03965&json=true","fetch_graph":"https://pith.science/api/pith-number/CSVM6P4V2UMQTIPPKBJMQFHQ6M/graph.json","fetch_events":"https://pith.science/api/pith-number/CSVM6P4V2UMQTIPPKBJMQFHQ6M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M/action/storage_attestation","attest_author":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M/action/author_attestation","sign_citation":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M/action/citation_signature","submit_replication":"https://pith.science/pith/CSVM6P4V2UMQTIPPKBJMQFHQ6M/action/replication_record"}},"created_at":"2026-05-18T00:48:52.556730+00:00","updated_at":"2026-05-18T00:48:52.556730+00:00"}