{"id":"W3196440680","doi":"10.1002/gepi.22430","title":"Block coordinate descent algorithm improves variable selection and estimation in error‐in‐variables regression","year":2021,"lang":"en","type":"article","venue":"Genetic Epidemiology","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; McGill University; Jewish General Hospital","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Medical Research Council; Compute Canada","keywords":"Coordinate descent; Statistics; Block (permutation group theory); Regression; Selection (genetic algorithm); Variable (mathematics); Estimation; Regression analysis; Standard error; Feature selection; Algorithm; Mathematics; Computer science; Artificial intelligence; Combinatorics; Engineering","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001734874,0.0001516947,0.0005057151,0.00008752603,0.00004970508,0.000008717742,0.00006187833,0.0002060064,0.00006806826],"category_scores_gemma":[0.01339599,0.0001310949,0.00002120625,0.00027315,0.00005741045,0.00003039059,0.0000758043,0.0002308323,0.000003068392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006474154,"about_ca_system_score_gemma":0.00008305781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000220365,"about_ca_topic_score_gemma":0.00007335064,"domain_scores_codex":[0.9973838,0.001141685,0.0006354549,0.0004082817,0.00006143173,0.0003692949],"domain_scores_gemma":[0.9953227,0.004194677,0.0001571405,0.000173062,0.00007834662,0.00007408829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003248385,0.0003023827,0.03509931,0.0003898862,0.00003535308,0.00004739071,0.0002908359,0.002379223,0.01318286,0.2357829,0.0007655344,0.7116919],"study_design_scores_gemma":[0.0002912583,0.00006064115,0.03377565,0.000116304,0.00001403922,0.00004943403,0.00002926087,0.3772072,0.0006365879,0.5876534,0.0000655545,0.0001007403],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.092459,0.0005889184,0.9059297,0.0003794857,0.0001742488,0.0001804627,0.000005807679,0.00002417539,0.0002581567],"genre_scores_gemma":[0.05053754,0.000156803,0.9489657,0.0001596784,0.00003028412,0.0000382788,0.000005610907,0.00001260498,0.00009349739],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7115911,"threshold_uncertainty_score":0.9949146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08093438238805208,"score_gpt":0.3925238854615996,"score_spread":0.3115895030735475,"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."}}