{"id":"W2887260238","doi":"10.1002/cjs.11459","title":"Variable selection for recurrent event data with broken adaptive ridge regression","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; National Institutes of Health; National Natural Science Foundation of China","keywords":"Oracle; Covariate; Computer science; Event (particle physics); Cluster analysis; Variable (mathematics); Regression; Feature selection; Selection (genetic algorithm); Regression analysis; Data mining; Artificial intelligence; Machine learning; Econometrics; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007620285,0.0001290434,0.0002617325,0.0001119834,0.0001952112,0.00005212105,0.0002962212,0.00005674228,0.0002580178],"category_scores_gemma":[0.003968281,0.00009359235,0.00001726418,0.000154837,0.0001369987,0.0001042549,0.00001827927,0.0001949369,0.00000370722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001594619,"about_ca_system_score_gemma":0.001544653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006564087,"about_ca_topic_score_gemma":0.01203241,"domain_scores_codex":[0.9988471,0.00009390396,0.0004071476,0.0001623313,0.0001977855,0.0002917259],"domain_scores_gemma":[0.9968067,0.001036901,0.0003892822,0.0002331749,0.001084752,0.0004491248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002608311,0.00003612347,0.0003622099,0.00007578207,0.00008745187,0.00001967772,0.0002529351,0.000004147974,0.00002603994,0.7440467,0.2043578,0.05047032],"study_design_scores_gemma":[0.0009975441,0.004927663,0.0008581151,0.0007662853,0.0003104363,0.0001908186,0.0002027365,0.03320141,0.0002239248,0.9087555,0.04925769,0.0003079237],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004256757,0.00005049103,0.9950249,0.00008348649,0.0004713511,0.0001777778,0.003314477,0.000004314572,0.0004474939],"genre_scores_gemma":[0.01936515,0.000005702201,0.9799834,0.00005774738,0.0003925156,0.000003264353,0.00003235705,0.00002241788,0.0001374649],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1647088,"threshold_uncertainty_score":0.6714367,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.168008427729278,"score_gpt":0.3766186943604906,"score_spread":0.2086102666312127,"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."}}