{"id":"W2986559674","doi":"10.1080/00949655.2019.1687700","title":"Semiparametric estimation for the transformation model with length-biased data and covariate measurement error","year":2019,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Covariate; Estimator; Transformation (genetics); Truncation (statistics); Mathematics; Inference; Statistics; Data transformation; Econometrics; Observational error; Statistical inference; Survival function; Algorithm; Computer science; Data mining; Artificial intelligence","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":[0.001651938,0.0001015418,0.0002227792,0.00008552676,0.0001027172,0.000089439,0.00007517403,0.00004166114,0.000009433686],"category_scores_gemma":[0.002203175,0.00006266732,0.00001526003,0.0001098909,0.00004527247,0.0002698926,0.00001434659,0.0001183254,5.825302e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003233711,"about_ca_system_score_gemma":0.00006221322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000234326,"about_ca_topic_score_gemma":0.000001450173,"domain_scores_codex":[0.9987276,0.00009730511,0.0005031675,0.0001300402,0.0004434347,0.00009848299],"domain_scores_gemma":[0.9933375,0.00565975,0.0003213081,0.0001035127,0.0005126766,0.00006531878],"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":[0.0003986286,0.00005226682,0.00007189035,0.0002035615,0.00005498197,4.365638e-7,0.0005314799,0.7678002,0.00003235498,0.09805919,0.00006762688,0.1327274],"study_design_scores_gemma":[0.001200332,0.0002878477,0.001926194,0.00005325276,0.000146669,0.000006954812,0.0001015995,0.8196324,0.000005596492,0.1765476,0.00002043823,0.00007112784],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02726672,0.00003751461,0.9717564,0.0002907535,0.00005250577,0.0004743988,0.00008289956,0.000008114758,0.00003072016],"genre_scores_gemma":[0.5637907,0.000005286815,0.4361382,0.00003344261,0.00001093718,0.000001769243,0.00001213627,0.000005585714,0.000002012172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5365239,"threshold_uncertainty_score":0.2637565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2958227208567343,"score_gpt":0.4354156090633897,"score_spread":0.1395928882066554,"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."}}