{"paper":{"title":"Multi-Fidelity Quantile Regression","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The high-fidelity quantile equals the low-fidelity quantile evaluated at a covariate-dependent level.","cross_cats":["stat.AP","stat.ML"],"primary_cat":"stat.ME","authors_text":"Yao Zhang, Yixiang Liu","submitted_at":"2026-05-11T11:43:38Z","abstract_excerpt":"High-fidelity (HF) data are often expensive to collect and therefore scarce, making conditional quantiles difficult to estimate accurately. We propose a two-stage, model-agnostic method for multi-fidelity quantile regression. The central idea is a local quantile link: at each covariate value, the HF quantile is represented as a low-fidelity (LF) quantile evaluated at a covariate-dependent level. This reformulation reduces the problem to estimating the level function, which can be smoother than the HF quantile itself when the LF and HF conditional distributions have similar shapes. We also stud"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The central idea is a local quantile link: at each covariate value, the HF quantile is represented as a low-fidelity (LF) quantile evaluated at a covariate-dependent level. This reformulation reduces the problem to estimating the level function, which can be smoother than the HF quantile itself when the LF and HF conditional distributions have similar shapes.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The LF and HF conditional distributions have similar shapes so that the level function is smoother than the target HF quantile surface.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The high-fidelity quantile equals the low-fidelity quantile evaluated at a covariate-dependent level.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2007cfc7bb88bec65763536a386fd4edf5af22ff49530026c94e3887e4632c8f"},"source":{"id":"2605.10406","kind":"arxiv","version":2},"verdict":{"id":"987b7075-334a-471a-b17b-97de10928032","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-12T05:09:22.553356Z","strongest_claim":"The central idea is a local quantile link: at each covariate value, the HF quantile is represented as a low-fidelity (LF) quantile evaluated at a covariate-dependent level. This reformulation reduces the problem to estimating the level function, which can be smoother than the HF quantile itself when the LF and HF conditional distributions have similar shapes.","one_line_summary":"A model-agnostic two-stage estimator links high-fidelity quantiles to low-fidelity ones via a covariate-dependent level function for faster convergence and better accuracy with limited high-fidelity data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The LF and HF conditional distributions have similar shapes so that the level function is smoother than the target HF quantile surface.","pith_extraction_headline":"The high-fidelity quantile equals the low-fidelity quantile evaluated at a covariate-dependent level."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.10406/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T06:02:01.017370Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T15:34:10.060621Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T11:31:18.116465Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T09:22:33.588465Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"4570045fccd949425672731e52547527378d781800e2e7b19733f924f1413c2d"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"220ecfb182bf375af9d222334a98a0b2e91febce9d86d161dbb0052f7cf491a8"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}