{"id":"W3122594583","doi":"","title":"Nonparametric density estimation for multivariate bounded data","year":2007,"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":"Center for Interuniversity Research and Analysis on Organizations; HEC Montréal; Université de Montréal","funders":"","keywords":"Estimator; Bounded function; Mathematics; Kernel density estimation; Multivariate kernel density estimation; Variable kernel density estimation; Nonparametric statistics; Applied mathematics; Kernel (algebra); Mathematical optimization; Statistics; Kernel method; Computer science; Support vector machine; Discrete mathematics; Artificial intelligence; Mathematical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009165145,0.0003132651,0.0007404528,0.000629539,0.0001781232,0.0002256879,0.001283817,0.0005731162,0.00005977057],"category_scores_gemma":[0.04489465,0.0003252671,0.0001067425,0.0002188795,0.0002634809,0.0001005896,0.002238205,0.001475367,0.000009569311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007257304,"about_ca_system_score_gemma":0.0005579602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001662672,"about_ca_topic_score_gemma":0.0001892337,"domain_scores_codex":[0.9963408,0.0003840951,0.0009468059,0.001185816,0.0003276029,0.0008148432],"domain_scores_gemma":[0.9812638,0.01560753,0.0003260554,0.002300492,0.0002954344,0.0002067162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002253912,0.000330341,0.0005035335,0.0008794713,0.0001193208,0.00001521517,0.0001478397,0.000523282,0.0000379034,0.07203598,0.0002354147,0.9249463],"study_design_scores_gemma":[0.0004814212,0.00005878542,0.002053357,0.0001557321,0.00002295442,0.000003113467,0.00004876107,0.4693843,0.0001144943,0.5263103,0.001070887,0.000295927],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.173642,0.00008947874,0.7762275,0.0003324747,0.001598073,0.005710155,0.001889391,0.0001827525,0.04032812],"genre_scores_gemma":[0.07360109,0.0004408644,0.924817,0.00003984045,0.0001828972,0.0001707021,0.0002920352,0.00007120631,0.0003843179],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9246504,"threshold_uncertainty_score":0.99992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2850737200650736,"score_gpt":0.4943127845682242,"score_spread":0.2092390645031506,"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."}}