{"id":"W1581220891","doi":"10.2139/ssrn.2435478","title":"Semiparametric Localized Bandwidth Selection in Kernel Density Estimation","year":2014,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Universitetet i Bergen; Australian Research Council; Monash University; York University","keywords":"Kernel density estimation; Bandwidth (computing); Selection (genetic algorithm); Kernel (algebra); Semiparametric model; Computer science; Statistics; Mathematics; Econometrics; Artificial intelligence; Parametric statistics; Telecommunications; Estimator; Combinatorics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004030778,0.0001408832,0.0002753877,0.0002222127,0.0001129958,0.00005086794,0.0001338878,0.0001018298,0.00004758355],"category_scores_gemma":[0.005464268,0.0001207512,0.00006058809,0.0005321698,0.00003161648,0.000101145,0.00001886705,0.001354656,0.00002358675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000707032,"about_ca_system_score_gemma":0.0004227017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007758259,"about_ca_topic_score_gemma":0.0003702407,"domain_scores_codex":[0.9975698,0.0003934399,0.0003958063,0.0001834241,0.0002678045,0.001189739],"domain_scores_gemma":[0.9985904,0.0009484502,0.0001818329,0.00009739938,0.0001030244,0.00007889598],"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":[0.0000519005,0.00008725324,0.003629647,0.00001622567,0.00002501668,8.731108e-7,0.00004786704,0.0001270465,0.0001703938,0.8468525,0.00009770372,0.1488935],"study_design_scores_gemma":[0.0006654019,0.0002296231,0.002595868,0.00002403281,0.00002747217,0.0001659608,0.00004023347,0.07856888,0.0002521787,0.9171994,0.00009835944,0.0001326285],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2271843,0.00005942053,0.7719501,0.0001016723,0.00006737383,0.00008305757,4.166519e-7,0.00002967447,0.0005240295],"genre_scores_gemma":[0.8896465,0.0001459628,0.1097996,0.00006486174,0.00009321971,0.000004883365,8.015019e-7,0.00001731107,0.0002269057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6624622,"threshold_uncertainty_score":0.6541634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.024974979566604,"score_gpt":0.329481039797333,"score_spread":0.304506060230729,"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."}}