{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005428408,0.0004992166,0.0009744741,0.0009569237,0.000436939,0.0007290109,0.001463158,0.0006867289,0.001264992],"category_scores_gemma":[0.01186095,0.0004530117,0.0010315,0.0008717926,0.001219265,0.0007713329,0.002332424,0.001757175,0.0002338315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005060406,"about_ca_system_score_gemma":0.001852514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002331087,"about_ca_topic_score_gemma":0.002492474,"domain_scores_codex":[0.9967674,0.002216276,0.00006530036,0.0002771106,0.0005410713,0.0001327305],"domain_scores_gemma":[0.9950055,0.003677701,0.0004206126,0.0004262819,0.0003647901,0.0001051118],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001361574,0.0001362378,0.004191091,0.000136611,0.0002581865,0.0001509342,0.0001605082,0.6983428,0.004175944,0.1600757,0.002097788,0.1301381],"study_design_scores_gemma":[0.00001051629,0.00002517689,0.0005008623,0.000006603282,0.000007359976,0.00002153627,0.000007254956,0.9743643,0.0004059279,0.02410368,0.0005380914,0.000008732797],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008172452,0.0000929288,0.9913716,0.000140239,0.000009537741,0.00001079716,0.00002205708,0.0000345075,0.0001458942],"genre_scores_gemma":[0.4096048,0.0005584491,0.5861915,0.0002261163,0.0001260938,0.0002655786,0.0003354608,0.00006498102,0.002626979],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005428408,"threshold_uncertainty_score":0.02870846,"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."}}