{"id":"W2042555191","doi":"10.1198/jbes.2011.09159","title":"Data-Driven Bandwidth Selection for Nonstationary Semiparametric Models","year":2011,"lang":"en","type":"article","venue":"Journal of Business and Economic Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Bandwidth (computing); Semiparametric regression; Monte Carlo method; Computer science; Contrast (vision); Mathematics; Semiparametric model; Applied mathematics; Econometrics; Regression; Mathematical optimization; Statistics; Artificial intelligence; Telecommunications; Estimator","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":[0.009776548,0.0005549603,0.0007188044,0.001726054,0.0003605009,0.0009946882,0.001595721,0.0009970024,0.001308805],"category_scores_gemma":[0.05983444,0.0004565726,0.0007273594,0.001186379,0.001473321,0.001917436,0.001944823,0.001946743,0.0002720011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007671064,"about_ca_system_score_gemma":0.001267298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001199074,"about_ca_topic_score_gemma":0.0008970817,"domain_scores_codex":[0.994832,0.003572318,0.0001645182,0.0004483248,0.0008436408,0.0001391126],"domain_scores_gemma":[0.9692585,0.02477473,0.001801714,0.002225388,0.001698875,0.0002408069],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002125557,0.000171332,0.00556607,0.0003183909,0.000237579,0.0002213195,0.0004954237,0.4643562,0.004782537,0.3582527,0.001528451,0.1638575],"study_design_scores_gemma":[0.00003020456,0.00004794995,0.0009247149,0.00002429209,0.00002472155,0.00005228405,0.0000250014,0.8991193,0.001482942,0.09722921,0.001012994,0.00002639216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01832207,0.0001396843,0.9809132,0.00009246587,0.0000149898,0.00001518844,0.00001572969,0.0001135961,0.0003729692],"genre_scores_gemma":[0.7293986,0.0004578753,0.267795,0.0001529926,0.00009976391,0.0002689579,0.0002520487,0.0002176311,0.001357173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009776548,"threshold_uncertainty_score":0.05170393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2772004591496926,"score_gpt":0.3597163242645062,"score_spread":0.08251586511481362,"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."}}