{"id":"W3145349353","doi":"10.1214/21-ejs1831","title":"Graphical-model based high dimensional generalized linear models","year":2021,"lang":"en","type":"article","venue":"Electronic Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Crohn's and Colitis Canada; Leona M. and Harry B. Helmsley Charitable Trust","keywords":"Mathematics; Estimator; Model selection; Linear model; Generalized linear model; Graphical model; Lasso (programming language); Consistency (knowledge bases); Curse of dimensionality; Node (physics); Clustering high-dimensional data; Dimensionality reduction; Graph; High dimensional; Algorithm; Mathematical optimization; Applied mathematics; Computer science; Machine learning; Statistics; Artificial intelligence; Cluster analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004092115,0.001285919,0.002052426,0.001457612,0.0004648188,0.001833877,0.003181058,0.001693477,0.003022193],"category_scores_gemma":[0.01764088,0.0008251864,0.001723231,0.0024731,0.001825169,0.002186621,0.002211042,0.002854275,0.0009357571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008886132,"about_ca_system_score_gemma":0.001400736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004413071,"about_ca_topic_score_gemma":0.004260613,"domain_scores_codex":[0.9960551,0.002535645,0.00009959647,0.0006806865,0.0004473132,0.0001816717],"domain_scores_gemma":[0.9913803,0.006013615,0.001006504,0.00086454,0.0005656031,0.0001693277],"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":[0.0000728198,0.00005321989,0.002612252,0.0002018623,0.0002503306,0.0001990639,0.0001078167,0.8168369,0.0007421615,0.1452435,0.002082571,0.03159735],"study_design_scores_gemma":[0.0000120426,0.00002974181,0.0002753023,0.00001366818,0.00002553139,0.00003040661,0.00001174525,0.9232455,0.0001159881,0.07546082,0.0007665943,0.00001260495],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005584667,0.00025764,0.9929462,0.0003569828,0.00004124246,0.00002263817,0.0001666593,0.0001478281,0.0004760619],"genre_scores_gemma":[0.5253444,0.001624251,0.4645519,0.0007802108,0.0004084026,0.0005284273,0.001547003,0.0001748953,0.005040507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004413071,"threshold_uncertainty_score":0.02164143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05851530656828702,"score_gpt":0.3404529522454549,"score_spread":0.2819376456771679,"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."}}