{"id":"W1566165412","doi":"","title":"On the estimation of hazard models with flexible baseline hazards and nonparametric unobserved heterogeneity","year":2003,"lang":"en","type":"article","venue":"Economics bulletin","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"sort; Baseline (sea); Nonparametric statistics; Hazard; Econometrics; Estimation; Computer science; Proportional hazards model; Specification; Statistics; Hazard ratio; Mathematics; Engineering; Confidence interval","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":[0.003292677,0.0001351463,0.0002555695,0.0001407723,0.00008737801,0.00009082513,0.0002626653,0.00005782675,0.0001950378],"category_scores_gemma":[0.002313234,0.000080985,0.000049906,0.0002424749,0.0001049258,0.00005459343,0.00003254902,0.00009781277,0.00006043456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003352693,"about_ca_system_score_gemma":0.00007475278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000999879,"about_ca_topic_score_gemma":0.00000374323,"domain_scores_codex":[0.9987574,0.0001516927,0.0004253222,0.0003271301,0.0001824129,0.0001560387],"domain_scores_gemma":[0.9970055,0.002148389,0.0001820236,0.000503682,0.00009191589,0.00006846062],"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":[0.00003459343,0.00002995735,0.0001123589,0.000003856358,0.00001246286,3.760776e-7,0.00002405281,0.9000022,0.000004631303,0.09681737,0.001209352,0.001748724],"study_design_scores_gemma":[0.0003901063,0.0001434446,0.0003714569,0.00001620658,0.00001278398,0.000007811727,0.00003035876,0.9273479,0.001645749,0.06702881,0.002854399,0.0001509976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.520153,0.00009393804,0.4769416,0.0007951318,0.00007350125,0.0001788977,0.00001300363,0.00001819467,0.001732645],"genre_scores_gemma":[0.9674954,0.00002245933,0.0319259,0.0001828279,0.000009691072,0.00001348782,0.000001596539,0.00001234227,0.0003363355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4473423,"threshold_uncertainty_score":0.3302472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07349646085740313,"score_gpt":0.2719194194510167,"score_spread":0.1984229585936136,"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."}}