{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:S3UFAYFLFPXEQD4W3U62ZKTER2","short_pith_number":"pith:S3UFAYFL","schema_version":"1.0","canonical_sha256":"96e85060ab2bee480f96dd3dacaa648eb8e667d416da1d7b14e7590a7929a583","source":{"kind":"arxiv","id":"2509.01478","version":1},"attestation_state":"computed","paper":{"title":"Handling Sparse Non-negative Data in Finance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"econ.EM","authors_text":"Agostino Capponi, Zhaonan Qu","submitted_at":"2025-09-01T13:46:27Z","abstract_excerpt":"We show that Poisson regression, though often recommended over log-linear regression for modeling count and other non-negative variables in finance and economics, can be far from optimal when heteroskedasticity and sparsity -- two common features of such data -- are both present. We propose a general class of moment estimators, encompassing Poisson regression, that balances the bias-variance trade-off under these conditions. A simple cross-validation procedure selects the optimal estimator. Numerical simulations and applications to corporate finance data reveal that the best choice varies subs"},"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":"2509.01478","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"econ.EM","submitted_at":"2025-09-01T13:46:27Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"057a3869ea3147f345c4dce0ba6ba8c49f942df0102e51fa05e1a75795f92cfe","abstract_canon_sha256":"4c2b8bd1c969c008b859e595427f49f1a3244e74d502613efc10d582652b3988"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:01.706708Z","signature_b64":"Pt9xfmCrAxgI7ftrG31Evoglxxm8Xln5Jzq7bkuM6JDtOgSOjBVWRC1+gKn5Agn/5jgeFSYUkNE2YwGcZp9DDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96e85060ab2bee480f96dd3dacaa648eb8e667d416da1d7b14e7590a7929a583","last_reissued_at":"2026-07-05T12:03:01.706104Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:01.706104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Handling Sparse Non-negative Data in Finance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"econ.EM","authors_text":"Agostino Capponi, Zhaonan Qu","submitted_at":"2025-09-01T13:46:27Z","abstract_excerpt":"We show that Poisson regression, though often recommended over log-linear regression for modeling count and other non-negative variables in finance and economics, can be far from optimal when heteroskedasticity and sparsity -- two common features of such data -- are both present. We propose a general class of moment estimators, encompassing Poisson regression, that balances the bias-variance trade-off under these conditions. A simple cross-validation procedure selects the optimal estimator. Numerical simulations and applications to corporate finance data reveal that the best choice varies subs"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.01478","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/2509.01478/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":"2509.01478","created_at":"2026-07-05T12:03:01.706188+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.01478v1","created_at":"2026-07-05T12:03:01.706188+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.01478","created_at":"2026-07-05T12:03:01.706188+00:00"},{"alias_kind":"pith_short_12","alias_value":"S3UFAYFLFPXE","created_at":"2026-07-05T12:03:01.706188+00:00"},{"alias_kind":"pith_short_16","alias_value":"S3UFAYFLFPXEQD4W","created_at":"2026-07-05T12:03:01.706188+00:00"},{"alias_kind":"pith_short_8","alias_value":"S3UFAYFL","created_at":"2026-07-05T12:03:01.706188+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/S3UFAYFLFPXEQD4W3U62ZKTER2","json":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2.json","graph_json":"https://pith.science/api/pith-number/S3UFAYFLFPXEQD4W3U62ZKTER2/graph.json","events_json":"https://pith.science/api/pith-number/S3UFAYFLFPXEQD4W3U62ZKTER2/events.json","paper":"https://pith.science/paper/S3UFAYFL"},"agent_actions":{"view_html":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2","download_json":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2.json","view_paper":"https://pith.science/paper/S3UFAYFL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.01478&json=true","fetch_graph":"https://pith.science/api/pith-number/S3UFAYFLFPXEQD4W3U62ZKTER2/graph.json","fetch_events":"https://pith.science/api/pith-number/S3UFAYFLFPXEQD4W3U62ZKTER2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2/action/storage_attestation","attest_author":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2/action/author_attestation","sign_citation":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2/action/citation_signature","submit_replication":"https://pith.science/pith/S3UFAYFLFPXEQD4W3U62ZKTER2/action/replication_record"}},"created_at":"2026-07-05T12:03:01.706188+00:00","updated_at":"2026-07-05T12:03:01.706188+00:00"}