{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:RPBQJHPDLJCKHBWPRBQ5E7PYW4","short_pith_number":"pith:RPBQJHPD","schema_version":"1.0","canonical_sha256":"8bc3049de35a44a386cf8861d27df8b7306be8f140597ac6936e021f510a96b2","source":{"kind":"arxiv","id":"1904.01849","version":1},"attestation_state":"computed","paper":{"title":"Measurement error induced by locational uncertainty when estimating discrete choice models with a distance as a regressor","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Carrie B. Dolan, Giuseppe Arbia, Paolo Berta","submitted_at":"2019-04-03T08:50:55Z","abstract_excerpt":"Spatial microeconometric studies typically suffer from various forms of inaccuracies that are not present when dealing with the classical regional spatial econometrics models. Among those, missing data, locational errors, sampling without a formal sample design, measurement errors and misalignment are the typical sources of inaccuracy that can affects the results in a spatial microeconometric analysis. In this paper, we have examined the effects of measurement error introduced in a logistic model by random geo-masking, when distances are used as predictors. Extending the classical results on t"},"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":"1904.01849","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-04-03T08:50:55Z","cross_cats_sorted":[],"title_canon_sha256":"9643ad5f3de365b6960fb765206dd8aca7bc08c5f3beb86c6035e29bccb734af","abstract_canon_sha256":"661b0b229906a08dd7011d62dee526ec9b230736a18a6efccfb3fcc6c085cdb2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:29.476047Z","signature_b64":"5BY6k40wNhHHHpWaHY+4drS8o2ik1HRei3M2KB9I4vIDxOU8SR6nYkRX8EPVOqg2PrOBtvdoKQSxzAjUoocbBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8bc3049de35a44a386cf8861d27df8b7306be8f140597ac6936e021f510a96b2","last_reissued_at":"2026-05-17T23:49:29.475442Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:29.475442Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Measurement error induced by locational uncertainty when estimating discrete choice models with a distance as a regressor","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Carrie B. Dolan, Giuseppe Arbia, Paolo Berta","submitted_at":"2019-04-03T08:50:55Z","abstract_excerpt":"Spatial microeconometric studies typically suffer from various forms of inaccuracies that are not present when dealing with the classical regional spatial econometrics models. Among those, missing data, locational errors, sampling without a formal sample design, measurement errors and misalignment are the typical sources of inaccuracy that can affects the results in a spatial microeconometric analysis. In this paper, we have examined the effects of measurement error introduced in a logistic model by random geo-masking, when distances are used as predictors. Extending the classical results on t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.01849","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":"1904.01849","created_at":"2026-05-17T23:49:29.475533+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.01849v1","created_at":"2026-05-17T23:49:29.475533+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.01849","created_at":"2026-05-17T23:49:29.475533+00:00"},{"alias_kind":"pith_short_12","alias_value":"RPBQJHPDLJCK","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"RPBQJHPDLJCKHBWP","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"RPBQJHPD","created_at":"2026-05-18T12:33:27.125529+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/RPBQJHPDLJCKHBWPRBQ5E7PYW4","json":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4.json","graph_json":"https://pith.science/api/pith-number/RPBQJHPDLJCKHBWPRBQ5E7PYW4/graph.json","events_json":"https://pith.science/api/pith-number/RPBQJHPDLJCKHBWPRBQ5E7PYW4/events.json","paper":"https://pith.science/paper/RPBQJHPD"},"agent_actions":{"view_html":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4","download_json":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4.json","view_paper":"https://pith.science/paper/RPBQJHPD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.01849&json=true","fetch_graph":"https://pith.science/api/pith-number/RPBQJHPDLJCKHBWPRBQ5E7PYW4/graph.json","fetch_events":"https://pith.science/api/pith-number/RPBQJHPDLJCKHBWPRBQ5E7PYW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4/action/storage_attestation","attest_author":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4/action/author_attestation","sign_citation":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4/action/citation_signature","submit_replication":"https://pith.science/pith/RPBQJHPDLJCKHBWPRBQ5E7PYW4/action/replication_record"}},"created_at":"2026-05-17T23:49:29.475533+00:00","updated_at":"2026-05-17T23:49:29.475533+00:00"}