{"paper":{"title":"Statistical Inference for Gaussian Kernel Robust Regression with the gkrreg Package","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Eufr\\'asio de A. Lima Neto, Marcelo R. Portela Ferreira","submitted_at":"2026-06-30T13:30:46Z","abstract_excerpt":"The Gaussian Kernel Robust Regression method (GKRReg) is a robust regression estimator that iteratively re-weights observations via a Gaussian kernel so that outliers and leverage points receive near-zero weight, with convergence of the estimation algorithm theoretically guaranteed. Despite a thorough study of estimation, the original work leaves open the problem of statistical inference for the regression coefficients. We fill this gap with three contributions. First, we formally establish that GKRReg belongs to the family of redescending M-estimators, providing the theoretical foundation for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31652","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/2606.31652/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"}