{"id":"W4318764290","doi":"10.1093/jrsssb/qkad001","title":"On inference in high-dimensional regression","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series B (Statistical Methodology)","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Regression diagnostic; Mathematics; Inference; Regression analysis; Lasso (programming language); Linear regression; Regression; Transformation (genetics); Design matrix; Econometrics; Set (abstract data type); Feature selection; Cross-sectional regression; Statistics; Computer science; Polynomial regression; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.05367405,0.001993977,0.00412259,0.003003757,0.001362506,0.003879188,0.004513163,0.003404715,0.004065453],"category_scores_gemma":[0.1659562,0.001591812,0.003308241,0.003424957,0.008496937,0.005250701,0.006456934,0.007818711,0.0009224944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003014873,"about_ca_system_score_gemma":0.002845893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004865464,"about_ca_topic_score_gemma":0.003151057,"domain_scores_codex":[0.9606906,0.03143498,0.001258129,0.002780877,0.003303274,0.000532097],"domain_scores_gemma":[0.760623,0.220503,0.004508927,0.008691756,0.00481878,0.0008546234],"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":[0.0001058365,0.00006996236,0.001199913,0.0004738875,0.000270878,0.0002336509,0.0003007783,0.2483523,0.0005474689,0.713058,0.002180737,0.0332065],"study_design_scores_gemma":[0.00002076116,0.00002969445,0.0001848577,0.00006749565,0.0000179897,0.00002724788,0.00002513261,0.5036492,0.0001922412,0.4943618,0.001401309,0.00002232658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001268724,0.0003421855,0.9969441,0.0006500572,0.00005836635,0.00002274292,0.00005030501,0.0000678868,0.0005956142],"genre_scores_gemma":[0.1537453,0.002330178,0.8356716,0.001573679,0.001630991,0.0008785735,0.0004911097,0.0002743554,0.003404211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05367405,"threshold_uncertainty_score":0.283859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1897421807401689,"score_gpt":0.4576779020452492,"score_spread":0.2679357213050803,"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."}}