{"id":"W4388499607","doi":"10.1080/02331888.2023.2280072","title":"Generalized log-logistic proportional hazard model: a non-penalty shrinkage approach","year":2023,"lang":"en","type":"article","venue":"Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Estimator; Shrinkage; Logistic regression; Weibull distribution; Subspace topology; Dimension (graph theory); Proportional hazards model; Econometrics; Combinatorics","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":[0.008336093,0.0008493274,0.001397492,0.0009440923,0.0003225352,0.0009081811,0.002928356,0.001041887,0.002384125],"category_scores_gemma":[0.01898511,0.0005147856,0.001117031,0.001245421,0.001195997,0.00187318,0.001939214,0.002034186,0.0007061021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005154039,"about_ca_system_score_gemma":0.001204729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001410736,"about_ca_topic_score_gemma":0.001097669,"domain_scores_codex":[0.9958115,0.002835819,0.00009987158,0.0004402892,0.0006727444,0.0001399081],"domain_scores_gemma":[0.9936283,0.004307764,0.000570297,0.0007887981,0.0005800285,0.0001246964],"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":[0.0002772643,0.0001323359,0.007613181,0.0003506858,0.0002303074,0.000488346,0.000333152,0.5951132,0.002796545,0.1587233,0.004742407,0.2291994],"study_design_scores_gemma":[0.00002818334,0.00007653615,0.0009135506,0.00002318407,0.00003351138,0.0001754024,0.00003479923,0.9409083,0.0005264418,0.05457121,0.002682886,0.00002602978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007654162,0.0001670932,0.9910538,0.0001972122,0.0000224255,0.00004999825,0.00006543939,0.00012346,0.000666426],"genre_scores_gemma":[0.486674,0.001319577,0.4992698,0.0003427682,0.0003163653,0.0008949108,0.0007659565,0.0002229399,0.01019376],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008336093,"threshold_uncertainty_score":0.04408604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1546916712143896,"score_gpt":0.4014049911385136,"score_spread":0.246713319924124,"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."}}