{"id":"W1971149402","doi":"10.1002/sim.3104","title":"Using the bootstrap to improve estimation and confidence intervals for regression coefficients selected using backwards variable elimination","year":2007,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; University of Toronto","funders":"Institute of Health Services and Policy Research; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Statistics; Confidence interval; Regression analysis; Mathematics; Regression; Percentile; Linear regression; Segmented regression; Robust confidence intervals; Variable (mathematics); Econometrics; Polynomial regression","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":[0.002541361,0.000153138,0.0003019296,0.0001211831,0.0001459171,0.00001726136,0.00009353671,0.00006882023,0.00001396817],"category_scores_gemma":[0.01480725,0.0001046689,0.000008601477,0.0003122678,0.0001384523,0.00006047552,0.00004066126,0.0001529212,1.852737e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001236635,"about_ca_system_score_gemma":0.00005185189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006215228,"about_ca_topic_score_gemma":0.00002578591,"domain_scores_codex":[0.9985008,0.0000960829,0.0005440218,0.0002637382,0.0002992433,0.0002960845],"domain_scores_gemma":[0.9950715,0.004021905,0.0002017312,0.00017211,0.0004364588,0.0000963225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002821952,0.00008183115,0.00003236003,0.0005273448,0.00002153509,0.00001027666,0.002493383,0.003116856,0.05438362,0.835196,0.0004689798,0.1033856],"study_design_scores_gemma":[0.0005555461,0.0001858271,0.0001067657,0.0004895768,0.00006002816,0.000006789813,0.0002842033,0.5712724,0.001190412,0.4257232,0.00003182719,0.00009346117],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006412495,0.0000382837,0.9921194,0.00009294094,0.0002817244,0.0008431048,0.0001233119,0.00001722312,0.00007148935],"genre_scores_gemma":[0.1297477,0.000005708983,0.8699679,0.0001263911,0.0000650337,0.0000117507,0.00001636749,0.0000205752,0.00003857584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5681555,"threshold_uncertainty_score":0.9934915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1800998728779016,"score_gpt":0.5190475704760493,"score_spread":0.3389476975981477,"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."}}