{"id":"W2117501165","doi":"","title":"Bandwidth Selection for Semiparametric Estimators Using the m-out-of-n Bootstrap ⁄","year":2006,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Estimator; Smoothing; Kernel density estimation; Mathematics; Bandwidth (computing); Semiparametric regression; Selection (genetic algorithm); Mean squared error; Statistics; Semiparametric model; Density estimation; Computer science; Artificial intelligence","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":[0.01240719,0.0005055811,0.0008767048,0.001467584,0.0005044735,0.001297451,0.001369525,0.001271042,0.002088212],"category_scores_gemma":[0.08688141,0.0004309762,0.000645877,0.001337908,0.001476125,0.002249196,0.001803191,0.001265573,0.0006130262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006492318,"about_ca_system_score_gemma":0.0006425492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000733591,"about_ca_topic_score_gemma":0.0007193801,"domain_scores_codex":[0.9922207,0.006216273,0.000194314,0.0004398925,0.0007964672,0.0001323015],"domain_scores_gemma":[0.9712865,0.02308754,0.001532571,0.002791816,0.001134486,0.0001670988],"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":[0.0002474109,0.0001140203,0.007140426,0.0004126369,0.0001841795,0.0002766834,0.0005251881,0.08947897,0.005360844,0.5723174,0.003660861,0.3202814],"study_design_scores_gemma":[0.00004398505,0.00009459345,0.003329064,0.0001708671,0.00003842922,0.0002329925,0.0001112308,0.6574231,0.002906608,0.3290048,0.006598161,0.00004630907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006831426,0.0002527879,0.991838,0.0001107153,0.00002302862,0.00002102693,0.00002040556,0.0001098353,0.0007927097],"genre_scores_gemma":[0.4918868,0.0007444721,0.5046513,0.0002053934,0.0002015052,0.0003874943,0.0002120994,0.0001912573,0.001519669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01240719,"threshold_uncertainty_score":0.06561625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2152920671901892,"score_gpt":0.4621781178197601,"score_spread":0.246886050629571,"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."}}