{"id":"W2096410324","doi":"10.1002/cjs.10042","title":"On minimum Hellinger distance estimation","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hellinger distance; Estimator; Robustness (evolution); Parametric statistics; Mathematics; Semiparametric model; Applied mathematics; Sample size determination; Semiparametric regression; Mathematical optimization; Statistics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002986881,0.0001234697,0.0002528846,0.0001261434,0.0001018001,0.00003955343,0.0001306192,0.00005094463,0.00009540394],"category_scores_gemma":[0.003001684,0.0001093351,0.00003937374,0.0000900737,0.00006227413,0.00007764207,0.000001592514,0.0002246248,0.000008442057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001281096,"about_ca_system_score_gemma":0.0003144369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002382375,"about_ca_topic_score_gemma":0.0004202966,"domain_scores_codex":[0.9989374,0.00005130062,0.0004468189,0.00009903384,0.0002032633,0.0002621994],"domain_scores_gemma":[0.9978657,0.0009619275,0.0002706395,0.000137819,0.0002511389,0.0005127924],"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":[0.00002240671,0.00002146094,0.00000458104,0.00001686905,0.000009313652,0.0002182734,0.0001933385,0.001010798,0.0000167893,0.9113801,0.01422351,0.0728826],"study_design_scores_gemma":[0.000239177,0.0003495135,0.0001064464,0.0001025785,0.00003291394,0.00003267449,0.0000250731,0.007608159,0.00005696383,0.9892616,0.00206313,0.0001218314],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001512724,0.00007759161,0.9962949,0.0003149739,0.0002704291,0.00006999228,0.0002878894,0.000005357498,0.001166159],"genre_scores_gemma":[0.2412521,0.00001028339,0.7581074,0.0003074073,0.00006990309,4.292182e-7,0.000003560302,0.00001248108,0.000236429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2397393,"threshold_uncertainty_score":0.4458555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07323263068428323,"score_gpt":0.3698762184012752,"score_spread":0.296643587716992,"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."}}