{"id":"W1934051277","doi":"10.1111/caje.12130","title":"Variable selection and estimation in high‐dimensional models","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Economics/Revue canadienne d économique","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Covariate; Econometrics; Nonparametric statistics; Monte Carlo method; Model selection; Variety (cybernetics); Variable (mathematics); Sample size determination; Semiparametric model; Semiparametric regression; Selection (genetic algorithm); Mathematics; Computer science; Statistics; Machine learning","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":[0.001121207,0.0001407431,0.0003909352,0.0003977188,0.00005212138,0.00006206198,0.000126755,0.0001215866,0.00007501225],"category_scores_gemma":[0.001211733,0.0001597143,0.00003129217,0.00009843007,0.00005606233,0.0003368922,0.00001030865,0.0002333178,0.00000247681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182945,"about_ca_system_score_gemma":0.001916688,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.09713058,"about_ca_topic_score_gemma":0.8567471,"domain_scores_codex":[0.9988032,0.00008075629,0.0005865961,0.0001772009,0.000002956239,0.0003492846],"domain_scores_gemma":[0.9978659,0.0003576667,0.0002845005,0.0001133469,0.0001927652,0.001185788],"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":[0.00003309334,0.000006375753,0.0006966456,0.00001579189,0.00001841498,0.00001813752,0.0002874345,0.03964362,0.000002980652,0.9567071,0.0002367354,0.002333738],"study_design_scores_gemma":[0.0004246965,0.0001439417,0.0002513177,0.00005377256,0.00001445054,0.0002221101,0.00005771449,0.2323703,0.000015217,0.7662003,0.0001195015,0.0001266775],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9653605,0.00004684178,0.03310524,0.0004215873,0.0004189185,0.0001143456,0.00004486136,0.000003270542,0.0004844351],"genre_scores_gemma":[0.7291155,0.000003654988,0.2706013,0.0001167857,0.00008640809,0.00000457785,0.000002457465,0.00001672563,0.00005258608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7596165,"threshold_uncertainty_score":0.9088817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2698001848241762,"score_gpt":0.2479042219993835,"score_spread":0.02189596282479275,"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."}}