{"id":"W2017956206","doi":"10.1016/j.jspi.2008.07.008","title":"Bayes estimation based on -record data from a general class of distributions under balanced type loss functions","year":2008,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Mathematics; Weibull distribution; Estimator; Bayes' theorem; Bayes estimator; Statistics; Type (biology); Exponential function; Mean squared error; Parametric statistics; Applied mathematics; Exponential family; Pareto distribution; Prior probability; Class (philosophy); Pareto principle; Exponential type; Estimation; Bayesian probability; Computer science; Mathematical analysis; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0001695189,0.0001180577,0.0002793438,0.00006588677,0.000166321,0.00002623661,0.0001639036,0.00006397389,0.0002982742],"category_scores_gemma":[0.003543317,0.00009861581,0.00002546881,0.0001760012,0.0002261335,0.0001393583,0.00003410387,0.0002321261,0.00001333282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003633988,"about_ca_system_score_gemma":0.0001619995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002065241,"about_ca_topic_score_gemma":0.000001897754,"domain_scores_codex":[0.9987653,0.00006447977,0.000580765,0.0001643702,0.0002939621,0.0001311002],"domain_scores_gemma":[0.9959727,0.00297378,0.0003412317,0.0002446041,0.0003214069,0.0001462891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003847983,0.0007969726,0.01320868,0.00007983834,0.0001106239,0.00003910984,0.0001086811,0.00703492,0.0003805451,0.9204493,0.05282718,0.004579384],"study_design_scores_gemma":[0.0008451017,0.0003145088,0.1711483,0.0002044921,0.0001244063,0.00002850371,0.00005354009,0.6909555,0.0000558951,0.1355912,0.0005072089,0.0001713667],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05893748,0.00001723099,0.935625,0.0003311488,0.00008724903,0.00006247746,0.004662947,0.00001714519,0.00025939],"genre_scores_gemma":[0.8580608,0.00001010981,0.140858,0.00006701185,0.00004249054,0.000002389923,0.0009300431,0.000006140887,0.00002295316],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7991234,"threshold_uncertainty_score":0.4241937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1603878237965399,"score_gpt":0.4047650518887529,"score_spread":0.244377228092213,"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."}}