{"id":"W4312600904","doi":"10.1007/978-3-031-08329-7_4","title":"Minimum Wasserstein Distance Estimator Under Finite Location-Scale Mixtures","year":2012,"lang":"en","type":"book-chapter","venue":"ICSA book series in statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Outlier; Estimator; Robustness (evolution); Computation; Mathematics; Scale (ratio); Population; Mathematical optimization; Applied mathematics; Computer science; Mixture model; Algorithm; Statistics","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004477046,0.0007636461,0.001071086,0.0001658816,0.0001619192,0.0001075241,0.0004514927,0.0005932724,0.002414151],"category_scores_gemma":[0.001711778,0.0007613832,0.00009424939,0.00008579295,0.000724429,0.000252284,0.0001799506,0.0008249709,0.0001800625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002638178,"about_ca_system_score_gemma":0.0002584893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001858269,"about_ca_topic_score_gemma":0.000270834,"domain_scores_codex":[0.9967586,0.000106001,0.001219958,0.0006160235,0.0006360637,0.0006633254],"domain_scores_gemma":[0.9929367,0.004841475,0.0006217699,0.0009229679,0.0004061942,0.0002708962],"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":[0.00006598313,0.00006147091,0.00003101257,0.0007757118,0.00006214685,0.00004578212,0.0003187398,0.00001847417,0.00000900008,0.9790505,0.01490675,0.004654499],"study_design_scores_gemma":[0.0002791488,0.0001079415,0.0000595196,0.000647551,0.0001666512,0.00001256297,0.00006976726,0.0005809003,0.000059698,0.8507633,0.146453,0.000799978],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000003584549,0.002300637,0.819133,0.000129618,0.0006985966,0.0005124136,0.003205013,0.0001006721,0.1739164],"genre_scores_gemma":[0.0005241975,0.001124816,0.7235634,0.0003140027,0.0002908637,0.00006506648,0.0002478398,0.0002187243,0.273651],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1315463,"threshold_uncertainty_score":0.9994837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0630540749450256,"score_gpt":0.3354654621384938,"score_spread":0.2724113871934682,"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."}}