{"id":"W2800000274","doi":"10.1080/00949655.2018.1472263","title":"Minimum Hellinger distance estimation for a semiparametric location-shifted mixture model","year":2018,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Hellinger distance; Outlier; Mathematics; Estimator; Parametric statistics; Robustness (evolution); Semiparametric model; Applied mathematics; Semiparametric regression; Parametric model; Algorithm; Mathematical optimization; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004905475,0.0008426267,0.001462336,0.001224543,0.0004573209,0.001383834,0.002265816,0.001551547,0.001433332],"category_scores_gemma":[0.0165137,0.0006630016,0.001068632,0.0009134724,0.001457012,0.003336076,0.002534938,0.001971289,0.0004602868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001065756,"about_ca_system_score_gemma":0.001182296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001975048,"about_ca_topic_score_gemma":0.001165075,"domain_scores_codex":[0.9977076,0.001146959,0.0001510477,0.0004696854,0.0004241302,0.0001005912],"domain_scores_gemma":[0.9929918,0.005198354,0.0004925449,0.0006168981,0.0005777287,0.0001226462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001988416,0.00005571649,0.002970199,0.0002532025,0.0001840561,0.0001895605,0.0002088995,0.7489665,0.004056985,0.1556241,0.001143168,0.08614879],"study_design_scores_gemma":[0.00001064152,0.00001969365,0.0002738268,0.00001104239,0.00001378519,0.00005262329,0.00001312715,0.9696286,0.0009350178,0.02837594,0.0006411606,0.00002456144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004583741,0.0001021714,0.9949725,0.00007651537,0.00000876735,0.00001312233,0.00002230999,0.00004675641,0.000174139],"genre_scores_gemma":[0.2933977,0.0005851446,0.7024573,0.0001960582,0.00006886477,0.0001814494,0.0004358547,0.00009569895,0.002581926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004905475,"threshold_uncertainty_score":0.02594298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754074039138239,"score_gpt":0.34382774534162,"score_spread":0.3162870049502376,"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."}}