{"id":"W172874229","doi":"10.1023/a:1020750810409","title":"Marginal and hazard ratio specific random data generation: Applications to semi-parametric bootstrapping","year":2002,"lang":"en","type":"article","venue":"Statistics and Computing","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Covariate; Censoring (clinical trials); Statistics; Bootstrapping (finance); Estimator; Event (particle physics); Mathematics; Parametric statistics; Computer science; Econometrics","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":[0.0004311133,0.0001540431,0.0002650907,0.00009262695,0.0003269625,0.0002598797,0.000166341,0.00004187772,0.0001031487],"category_scores_gemma":[0.0005270579,0.0001490779,0.00001070795,0.000265538,0.00007358182,0.00005502941,0.0001871238,0.0001374128,0.00001156929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001368402,"about_ca_system_score_gemma":0.00001183609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005014106,"about_ca_topic_score_gemma":0.000004857242,"domain_scores_codex":[0.998693,0.00008448868,0.0003717057,0.0004401502,0.0001806492,0.0002300117],"domain_scores_gemma":[0.997623,0.001657706,0.0001022519,0.0003767354,0.00008353985,0.0001568283],"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":[0.000005389408,0.00003671677,0.0001832234,0.00007731984,0.00001974903,0.0000078745,0.0001736904,0.00001261854,0.0001484032,0.5916974,0.01171989,0.3959177],"study_design_scores_gemma":[0.0006601432,0.00006794797,0.0008657218,0.00005094591,0.00005313394,0.00004143869,0.00007778841,0.8202537,0.00003217806,0.1696393,0.00793802,0.0003197266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001371552,0.000373919,0.9965287,0.0001703731,0.00008180585,0.0003596116,0.0003355178,0.00003604833,0.0007424685],"genre_scores_gemma":[0.1338813,0.00012501,0.8654892,0.00009561963,0.0002838983,0.00001139615,0.00003911329,0.00001471552,0.00005970224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.820241,"threshold_uncertainty_score":0.6079221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1842801634588534,"score_gpt":0.3748533784005708,"score_spread":0.1905732149417174,"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."}}