{"id":"W2892430151","doi":"10.1093/biomet/asy046","title":"A bootstrap recipe for post-model-selection inference under linear regression models","year":2018,"lang":"en","type":"article","venue":"Biometrika","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; Bootstrapping (finance); Estimator; Model selection; Recipe; Selection (genetic algorithm); Residual; Inference; Regression; Statistics; Applied mathematics; Econometrics; Artificial intelligence; Algorithm; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006103347,0.0001898464,0.0002752699,0.0004395348,0.0001767746,0.00004643343,0.0001916016,0.000193479,0.0001565089],"category_scores_gemma":[0.004186986,0.0001451087,0.0000857823,0.001032316,0.0001279229,0.0001390424,0.0000569384,0.0001232246,0.00003004805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006563202,"about_ca_system_score_gemma":0.0001123553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002355516,"about_ca_topic_score_gemma":0.00001142322,"domain_scores_codex":[0.9985894,0.00006956077,0.0003627521,0.0003583483,0.0002744011,0.0003455199],"domain_scores_gemma":[0.997233,0.001507768,0.0001514816,0.0002812533,0.0006936636,0.0001327998],"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":[0.000321884,0.0002884062,0.00005974096,0.0001563002,0.00005326124,6.641919e-7,0.000283249,0.00004828924,0.03588896,0.8576528,0.004499877,0.1007465],"study_design_scores_gemma":[0.0003563534,0.0006169713,0.0001170303,0.00007742053,0.00002988892,0.000002300682,0.00002682298,0.313195,0.01177164,0.6730805,0.0005311702,0.0001949598],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02883796,0.00004074875,0.9684277,0.0001896501,0.0002268811,0.0003582443,0.00008998405,0.0001172556,0.001711558],"genre_scores_gemma":[0.3901432,0.00001542897,0.6088649,0.0001503756,0.0001971685,0.00003691701,0.000007727333,0.00002399771,0.0005602695],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3613052,"threshold_uncertainty_score":0.5917359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3314746707499737,"score_gpt":0.4798864229770312,"score_spread":0.1484117522270574,"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."}}