{"id":"W3112197890","doi":"10.1080/24754269.2020.1858630","title":"<i>β</i>-divergence loss for the kernel density estimation with bias reduced","year":2020,"lang":"en","type":"article","venue":"Statistical Theory and Related Fields","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Kernel density estimation; Bandwidth (computing); Divergence (linguistics); Variable kernel density estimation; Kernel (algebra); Probability density function; Statistics; Computer science; Density estimation; Multivariate kernel density estimation; Algorithm; Cross-validation; Kernel method; Mathematics; Econometrics; Artificial intelligence; Telecommunications; Support vector machine","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":[0.01360702,0.001062221,0.001318812,0.00123944,0.0005483402,0.001541084,0.001745003,0.001747279,0.001601727],"category_scores_gemma":[0.04293608,0.0003403825,0.000893558,0.001153717,0.002479813,0.002806402,0.002507661,0.002640561,0.0006217168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001479196,"about_ca_system_score_gemma":0.001501137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001781543,"about_ca_topic_score_gemma":0.0006908057,"domain_scores_codex":[0.995554,0.002321369,0.0002130534,0.0005897336,0.001071651,0.0002501939],"domain_scores_gemma":[0.9865974,0.009299592,0.0007566325,0.001255487,0.001812259,0.0002786645],"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":[0.0003491271,0.0002268216,0.006883356,0.0009288845,0.000277383,0.0005089086,0.0004384218,0.2905812,0.008525684,0.4726813,0.008058406,0.2105406],"study_design_scores_gemma":[0.00002486124,0.0001263998,0.001738637,0.0001138583,0.00005251593,0.0004360299,0.00005090631,0.8435611,0.002268323,0.1479205,0.00366376,0.00004321128],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007278804,0.001029337,0.9898929,0.0004676981,0.00005560091,0.00002698667,0.00004335815,0.0001070204,0.001098316],"genre_scores_gemma":[0.4778228,0.003445551,0.5088534,0.001081976,0.0005742162,0.0005780453,0.0006542738,0.0004476605,0.006541999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01360702,"threshold_uncertainty_score":0.07196158,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07692395589857298,"score_gpt":0.3460699624121832,"score_spread":0.2691460065136103,"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."}}