{"id":"W2161960669","doi":"10.1002/cjs.10005","title":"New aspects of Bregman divergence in regression and classification with parametric and nonparametric estimation","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wisconsin Alumni Research Foundation; National Science Foundation","keywords":"Nonparametric statistics; Mathematics; Divergence (linguistics); Estimator; Parametric statistics; Nonparametric regression; Asymptotic distribution; Consistency (knowledge bases); Semiparametric regression; Bregman divergence; Regression analysis; Statistics; Covariance; Regression; Function (biology); Applied mathematics; Econometrics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0278601,0.001279247,0.001868869,0.002896842,0.001217476,0.004316288,0.002276513,0.002818532,0.001770478],"category_scores_gemma":[0.1091257,0.0008249631,0.001328072,0.003765448,0.007683197,0.009243959,0.006083689,0.007020064,0.000428373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00231328,"about_ca_system_score_gemma":0.002118966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002114253,"about_ca_topic_score_gemma":0.001323753,"domain_scores_codex":[0.9846273,0.01032455,0.0006403576,0.001107217,0.002891539,0.0004089546],"domain_scores_gemma":[0.9143317,0.06988011,0.003564463,0.005936038,0.005124134,0.0011636],"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":[0.00007662451,0.00005528217,0.002520867,0.00009624084,0.00006022833,0.0001544606,0.0003488464,0.06464104,0.0006528307,0.8890627,0.001695604,0.04063531],"study_design_scores_gemma":[0.00001407791,0.00003979963,0.0008392929,0.00004079313,0.0000102779,0.0001241615,0.00007088713,0.2902334,0.0002718428,0.7060519,0.002274634,0.00002888617],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01541398,0.001393909,0.9778728,0.001857291,0.00009973621,0.00002592232,0.0000442305,0.00007884255,0.003213297],"genre_scores_gemma":[0.6176007,0.002020492,0.3721017,0.001439537,0.0007103197,0.0002264324,0.0002774402,0.0002788301,0.005344518],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0278601,"threshold_uncertainty_score":0.14734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07712385356999514,"score_gpt":0.3589683068980273,"score_spread":0.2818444533280322,"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."}}