{"id":"W3125993413","doi":"10.1002/cjs.11593","title":"Variable selection and structure estimation for ultrahigh‐dimensional additive hazards models","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Model selection; Estimator; Computer science; Lasso (programming language); Consistency (knowledge bases); Mathematical optimization; Regularization (linguistics); Majorization; Algorithm; Mathematics; Artificial intelligence; Statistics","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":[0.0003085536,0.0001058476,0.0002581659,0.00008335177,0.0001511864,0.00007653298,0.00005273367,0.00007870579,0.0004746441],"category_scores_gemma":[0.005420642,0.00009679081,0.00002554006,0.0001130865,0.00005437006,0.000101925,0.000004665405,0.000180026,4.142537e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009628007,"about_ca_system_score_gemma":0.00175528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001732863,"about_ca_topic_score_gemma":0.001996447,"domain_scores_codex":[0.9990352,0.0000864796,0.0003997794,0.0001166009,0.0001673858,0.0001945032],"domain_scores_gemma":[0.9963871,0.001677267,0.0002316107,0.00006645588,0.001296194,0.0003413227],"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":[0.00001265892,0.000007758978,0.00002404504,0.00005612259,0.00004667394,0.00003009929,0.0001448751,0.001235565,0.0002100951,0.9655718,0.01603772,0.01662262],"study_design_scores_gemma":[0.0002781792,0.0001094014,0.0002495436,0.00005981372,0.00008661788,0.0002464698,0.00004763394,0.1477541,0.0005014511,0.8498905,0.00068317,0.00009304115],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003143454,0.00005783598,0.9911941,0.00009907745,0.0002693852,0.00008262372,0.004984667,0.000002692854,0.0001661143],"genre_scores_gemma":[0.0729065,0.000003487434,0.9267339,0.0001071172,0.00009446913,0.000001702973,0.00004584461,0.00001441772,0.00009256103],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1465186,"threshold_uncertainty_score":0.6489407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04280486349813785,"score_gpt":0.3028328362611771,"score_spread":0.2600279727630393,"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."}}