{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7O7BTNET7VT36GBHMNR2Y2C4TN","short_pith_number":"pith:7O7BTNET","schema_version":"1.0","canonical_sha256":"fbbe19b493fd67bf18276363ac685c9b7fec41b3f4291cb471603f32d40b3e88","source":{"kind":"arxiv","id":"2608.04860","version":1},"attestation_state":"computed","paper":{"title":"Nonparametric Goodness-of-fit Testing under Covariate Shift","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Dong Xia, Zhen Hou","submitted_at":"2026-08-05T13:51:43Z","abstract_excerpt":"This paper develops procedures for nonparametric goodness-of-fit testing under covariate shift, where labelled data are drawn from a source population but goodness-of-fit is evaluated for a target population. The distribution mismatch is quantified by either a bounded moment condition or a sub-exponential tail condition on the target-to-source density ratio. Our method combines truncated importance-weighting kernel ridge regression with a multiplier bootstrap to construct confidence sets for the regression function. The truncation stabilizes the importance- weighting kernel ridge regression as"},"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":"2608.04860","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2026-08-05T13:51:43Z","cross_cats_sorted":["cs.LG","math.ST","stat.ML","stat.TH"],"title_canon_sha256":"2c5f3971faa783dfda05b1590d56dcf84963fe497ca751770a38320bea4f365c","abstract_canon_sha256":"9dcce623c873f56db2cdf0c895234cc870ee547966127645e381efde5dfa87d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-06T01:47:24.859008Z","signature_b64":"udM7ozKoqowkjwBcXcK02L6l5CAEqRnd2pv/Z7fSTAXaNN6ME/gsuzNHoPW99JihXyxSe3YiNoGcvX9p9U7UAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbbe19b493fd67bf18276363ac685c9b7fec41b3f4291cb471603f32d40b3e88","last_reissued_at":"2026-08-06T01:47:24.857563Z","signature_status":"signed_v1","first_computed_at":"2026-08-06T01:47:24.857563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Goodness-of-fit Testing under Covariate Shift","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Dong Xia, Zhen Hou","submitted_at":"2026-08-05T13:51:43Z","abstract_excerpt":"This paper develops procedures for nonparametric goodness-of-fit testing under covariate shift, where labelled data are drawn from a source population but goodness-of-fit is evaluated for a target population. The distribution mismatch is quantified by either a bounded moment condition or a sub-exponential tail condition on the target-to-source density ratio. Our method combines truncated importance-weighting kernel ridge regression with a multiplier bootstrap to construct confidence sets for the regression function. The truncation stabilizes the importance- weighting kernel ridge regression as"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.04860","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/2608.04860/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":"2608.04860","created_at":"2026-08-06T01:47:24.859396+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.04860v1","created_at":"2026-08-06T01:47:24.859396+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.04860","created_at":"2026-08-06T01:47:24.859396+00:00"},{"alias_kind":"pith_short_12","alias_value":"7O7BTNET7VT3","created_at":"2026-08-06T01:47:24.859396+00:00"},{"alias_kind":"pith_short_16","alias_value":"7O7BTNET7VT36GBH","created_at":"2026-08-06T01:47:24.859396+00:00"},{"alias_kind":"pith_short_8","alias_value":"7O7BTNET","created_at":"2026-08-06T01:47:24.859396+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/7O7BTNET7VT36GBHMNR2Y2C4TN","json":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN.json","graph_json":"https://pith.science/api/pith-number/7O7BTNET7VT36GBHMNR2Y2C4TN/graph.json","events_json":"https://pith.science/api/pith-number/7O7BTNET7VT36GBHMNR2Y2C4TN/events.json","paper":"https://pith.science/paper/7O7BTNET"},"agent_actions":{"view_html":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN","download_json":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN.json","view_paper":"https://pith.science/paper/7O7BTNET","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.04860&json=true","fetch_graph":"https://pith.science/api/pith-number/7O7BTNET7VT36GBHMNR2Y2C4TN/graph.json","fetch_events":"https://pith.science/api/pith-number/7O7BTNET7VT36GBHMNR2Y2C4TN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN/action/storage_attestation","attest_author":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN/action/author_attestation","sign_citation":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN/action/citation_signature","submit_replication":"https://pith.science/pith/7O7BTNET7VT36GBHMNR2Y2C4TN/action/replication_record"}},"created_at":"2026-08-06T01:47:24.859396+00:00","updated_at":"2026-08-06T01:47:24.859396+00:00"}