{"id":"W4402782255","doi":"10.1007/s10463-024-00909-6","title":"Improved confidence intervals for nonlinear mixed-effects and nonparametric regression models","year":2024,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Statistics; Confidence interval; Nonparametric statistics; CDF-based nonparametric confidence interval; Nonparametric regression; Robust confidence intervals; Mixed model; Nonlinear regression; Econometrics; Regression analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.09632755,0.002445952,0.005011951,0.006114896,0.001049499,0.004942345,0.008719558,0.003933665,0.006535837],"category_scores_gemma":[0.3605762,0.001899185,0.004201937,0.00512135,0.003857065,0.007086067,0.004986315,0.009332757,0.001018994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002363671,"about_ca_system_score_gemma":0.002736972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004648193,"about_ca_topic_score_gemma":0.003301088,"domain_scores_codex":[0.9324541,0.05191196,0.002665044,0.005260721,0.006781871,0.0009263142],"domain_scores_gemma":[0.4556244,0.4995106,0.00898585,0.02287054,0.01145468,0.001553792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001397036,0.000288027,0.007087128,0.001527704,0.002178936,0.0006283631,0.0009699902,0.2293143,0.002033182,0.5267918,0.005155316,0.2226282],"study_design_scores_gemma":[0.0001499722,0.0002060725,0.002319113,0.0003156918,0.0005068012,0.0002383915,0.00008150872,0.7237731,0.001213887,0.2667298,0.004342035,0.0001235821],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004450958,0.00164056,0.992313,0.0002077583,0.0001070892,0.00003629419,0.0001484685,0.00038065,0.0007152543],"genre_scores_gemma":[0.2182861,0.002322997,0.7724414,0.0004178736,0.0008519465,0.0007252633,0.001483597,0.0008833903,0.002587416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09632755,"threshold_uncertainty_score":0.5094349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1617540427873668,"score_gpt":0.4517508378405108,"score_spread":0.289996795053144,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}