{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:FXINE5BXIPTQLY6GR6BPKBQDMI","short_pith_number":"pith:FXINE5BX","schema_version":"1.0","canonical_sha256":"2dd0d2743743e705e3c68f82f50603620cd10c6fc94f3977b39e3b7f77e3b806","source":{"kind":"arxiv","id":"1509.04836","version":1},"attestation_state":"computed","paper":{"title":"Jump detection in generalized error-in-variables regression with an application to Australian health tax policies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Jiti Gao, Peihua Qiu, Xiaodong Gong, Yicheng Kang","submitted_at":"2015-09-16T07:25:07Z","abstract_excerpt":"Without measurement errors in predictors, discontinuity of a nonparametric regression function at unknown locations could be estimated using a number of existing approaches. However, it becomes a challenging problem when the predictors contain measurement errors. In this paper, an error-in-variables jump point estimator is suggested for a nonparametric generalized error-in-variables regression model. A major feature of our method is that it does not impose any parametric distribution on the measurement error. Its performance is evaluated by both numerical studies and theoretical justifications"},"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":"1509.04836","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2015-09-16T07:25:07Z","cross_cats_sorted":[],"title_canon_sha256":"4c279d4a98b796cee2ca223a167375e57d595eff63e5a9244c05195cc0eebbcf","abstract_canon_sha256":"8cebe5facd4372c691c43875edddde3aa5161e91596da163285ce4adb605c1cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:32:51.537646Z","signature_b64":"xfg86IgEgDoh2fspc8se1TxxCvckiYLp+6OtX1PzZCii5F0dIur0SXJmxs9ml0S68ai32KuHiAkx8cbNzbixCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2dd0d2743743e705e3c68f82f50603620cd10c6fc94f3977b39e3b7f77e3b806","last_reissued_at":"2026-05-18T01:32:51.536955Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:32:51.536955Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Jump detection in generalized error-in-variables regression with an application to Australian health tax policies","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Jiti Gao, Peihua Qiu, Xiaodong Gong, Yicheng Kang","submitted_at":"2015-09-16T07:25:07Z","abstract_excerpt":"Without measurement errors in predictors, discontinuity of a nonparametric regression function at unknown locations could be estimated using a number of existing approaches. However, it becomes a challenging problem when the predictors contain measurement errors. In this paper, an error-in-variables jump point estimator is suggested for a nonparametric generalized error-in-variables regression model. A major feature of our method is that it does not impose any parametric distribution on the measurement error. Its performance is evaluated by both numerical studies and theoretical justifications"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.04836","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":"1509.04836","created_at":"2026-05-18T01:32:51.537051+00:00"},{"alias_kind":"arxiv_version","alias_value":"1509.04836v1","created_at":"2026-05-18T01:32:51.537051+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.04836","created_at":"2026-05-18T01:32:51.537051+00:00"},{"alias_kind":"pith_short_12","alias_value":"FXINE5BXIPTQ","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_16","alias_value":"FXINE5BXIPTQLY6G","created_at":"2026-05-18T12:29:22.688609+00:00"},{"alias_kind":"pith_short_8","alias_value":"FXINE5BX","created_at":"2026-05-18T12:29:22.688609+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/FXINE5BXIPTQLY6GR6BPKBQDMI","json":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI.json","graph_json":"https://pith.science/api/pith-number/FXINE5BXIPTQLY6GR6BPKBQDMI/graph.json","events_json":"https://pith.science/api/pith-number/FXINE5BXIPTQLY6GR6BPKBQDMI/events.json","paper":"https://pith.science/paper/FXINE5BX"},"agent_actions":{"view_html":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI","download_json":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI.json","view_paper":"https://pith.science/paper/FXINE5BX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1509.04836&json=true","fetch_graph":"https://pith.science/api/pith-number/FXINE5BXIPTQLY6GR6BPKBQDMI/graph.json","fetch_events":"https://pith.science/api/pith-number/FXINE5BXIPTQLY6GR6BPKBQDMI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI/action/storage_attestation","attest_author":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI/action/author_attestation","sign_citation":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI/action/citation_signature","submit_replication":"https://pith.science/pith/FXINE5BXIPTQLY6GR6BPKBQDMI/action/replication_record"}},"created_at":"2026-05-18T01:32:51.537051+00:00","updated_at":"2026-05-18T01:32:51.537051+00:00"}