{"id":"W4402448461","doi":"10.1080/00949655.2024.2399174","title":"Variable selection and estimation for recurrent event model with covariates subject to measurement error","year":2024,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Covariate; Mathematics; Statistics; Econometrics; Estimation; Event (particle physics); Subject (documents); Selection (genetic algorithm); Event data; Variable (mathematics); Observational error; Feature selection; Errors-in-variables models; Artificial intelligence; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00105465,0.0001031443,0.0002021041,0.0001231047,0.00008151095,0.0001248831,0.00002060019,0.0000375188,0.00001045926],"category_scores_gemma":[0.001778404,0.00007675854,0.00001712734,0.0001344934,0.0000178581,0.0001240071,0.000008186717,0.00009931264,4.563532e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008498154,"about_ca_system_score_gemma":0.00009800192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002300134,"about_ca_topic_score_gemma":0.000003211267,"domain_scores_codex":[0.9989087,0.00008139811,0.0004288657,0.0001443131,0.0003329899,0.0001037627],"domain_scores_gemma":[0.9973464,0.001813229,0.0001218612,0.00002605673,0.0005824794,0.0001099916],"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":[0.0003019519,0.00003961454,0.00001564354,0.0002489376,0.00003223028,8.635436e-7,0.0002410601,0.5567629,0.00007994434,0.3814845,0.0001366507,0.06065572],"study_design_scores_gemma":[0.0002567596,0.0006608388,0.0003272951,0.0001554519,0.00007742337,0.000008672827,0.00001387427,0.5832914,0.00001093861,0.4151195,0.00002078393,0.000057107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009028469,0.00004782281,0.9902574,0.0001933397,0.00009315425,0.0003125765,0.00002881945,0.00001882419,0.00001960177],"genre_scores_gemma":[0.4772917,0.000001183159,0.5226513,0.0000160857,0.00002231822,0.000004596653,0.000002800473,0.000006728714,0.000003317874],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4682633,"threshold_uncertainty_score":0.3130122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1286583142304459,"score_gpt":0.4289061137706419,"score_spread":0.300247799540196,"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."}}