{"id":"W3212249491","doi":"10.18280/mmep.080503","title":"E-Bayesian Estimation for Kumaraswamy Distribution Using Progressive First Failure Censoring","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Censoring (clinical trials); Bayesian probability; Statistics; Bayes estimator; Estimation; Mathematics; Computer science; Econometrics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.005536231,0.0007908254,0.001121795,0.001389641,0.0005220698,0.001492173,0.00144403,0.001238702,0.002416471],"category_scores_gemma":[0.01699656,0.0005828083,0.001031967,0.001216499,0.001479249,0.003360681,0.001921769,0.00203167,0.000465465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008259175,"about_ca_system_score_gemma":0.001180093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002530627,"about_ca_topic_score_gemma":0.00191806,"domain_scores_codex":[0.9979181,0.001019346,0.00008789461,0.0003217855,0.000512604,0.0001402814],"domain_scores_gemma":[0.9926257,0.005482386,0.0006744667,0.0004927835,0.0005850816,0.0001397131],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001319665,0.00008849678,0.005143699,0.0002481097,0.0001321791,0.000394949,0.0002607102,0.5535834,0.002499141,0.3502608,0.001469104,0.08578748],"study_design_scores_gemma":[0.00002061135,0.00004541568,0.001778565,0.00005069936,0.00002854361,0.0002173928,0.0000385094,0.8526054,0.0009520783,0.1425117,0.001705083,0.00004600785],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01105877,0.0003043867,0.9872731,0.0001606673,0.00001291131,0.00002103654,0.00004125429,0.00004991669,0.001077903],"genre_scores_gemma":[0.6830672,0.003190111,0.3053707,0.0003588664,0.0002461027,0.000259063,0.0005496474,0.0001122826,0.00684599],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005536231,"threshold_uncertainty_score":0.0292787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06434340945335333,"score_gpt":0.304965492164525,"score_spread":0.2406220827111716,"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."}}