{"id":"W2595665627","doi":"10.5539/ijsp.v6n3p24","title":"The Exponentiated Generalized Standardized Half-logistic Distribution","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Mathematics; Quantile; Estimator; Statistics; Bonferroni correction; Order statistic; Logistic distribution; Lorenz curve; Applied mathematics; Sample (material); Sample size determination; Distribution (mathematics); Econometrics; Logistic regression; Mathematical analysis; Inequality","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":[0.004120791,0.0006702656,0.0009932682,0.001487338,0.0005955878,0.001801909,0.003633985,0.001578542,0.0074469],"category_scores_gemma":[0.01684517,0.0003684825,0.001050146,0.001938315,0.002083644,0.004850816,0.001774463,0.001810852,0.001212314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001293386,"about_ca_system_score_gemma":0.001030575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002402116,"about_ca_topic_score_gemma":0.001653334,"domain_scores_codex":[0.9985927,0.000570546,0.00006067083,0.0002780381,0.0002763742,0.0002215512],"domain_scores_gemma":[0.9935693,0.002797465,0.001283972,0.001019242,0.0009255321,0.0004046139],"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":[0.0002106163,0.00006488375,0.007821372,0.0001346092,0.0000964084,0.0007622951,0.0006016499,0.1126937,0.001838572,0.8249695,0.005091917,0.04571453],"study_design_scores_gemma":[0.00006345907,0.0001054268,0.002227408,0.00004769565,0.00004318087,0.0007210126,0.000246839,0.5968665,0.0006214415,0.3928019,0.006189978,0.00006517307],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2120809,0.001055094,0.7717915,0.002094026,0.0001274025,0.0001488349,0.0007641223,0.0003474659,0.01159073],"genre_scores_gemma":[0.93129,0.001095555,0.05358423,0.000355882,0.0001403316,0.0002802744,0.0005560082,0.0001246742,0.01257308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0074469,"threshold_uncertainty_score":0.0249123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08577044287260789,"score_gpt":0.3968876932497301,"score_spread":0.3111172503771222,"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."}}