{"id":"W1979394579","doi":"10.1002/sim.1974","title":"Influence analysis for linear mixed‐effects models","year":2004,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences","funders":"National Cancer Institute","keywords":"Linear regression; Leverage (statistics); Mathematics; Measure (data warehouse); Linear model; Regression analysis; Statistics; Generalization; Regression; Applied mathematics; Simple linear regression; Generalized linear model; Infinitesimal; Simple (philosophy); Econometrics; Computer science; Data mining","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008262015,0.0001948097,0.0006561613,0.0002760584,0.00006122363,0.000009369632,0.000197231,0.00008744116,0.0000374338],"category_scores_gemma":[0.01072542,0.0001540147,0.00004656559,0.0007101258,0.0002251425,0.00005548766,0.00003194885,0.000197579,0.000004497555],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008124656,"about_ca_system_score_gemma":0.00005613663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001647265,"about_ca_topic_score_gemma":0.0001661272,"domain_scores_codex":[0.99835,0.00008886441,0.0005598644,0.0003191325,0.0003387946,0.0003433016],"domain_scores_gemma":[0.9935418,0.005643038,0.00014021,0.000324757,0.0002153085,0.0001348499],"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":[0.00002439173,0.00006906877,0.0003659011,0.0003101118,0.0001035488,0.00003984424,0.0004690094,0.002710856,0.00006533257,0.9902263,0.0003876711,0.005227968],"study_design_scores_gemma":[0.001264625,0.0002654322,0.003230203,0.0002005149,0.0004589355,0.000002000602,0.00005883647,0.03185428,0.0001499889,0.9623134,0.00003274591,0.0001690051],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005155232,0.0000557699,0.9933631,0.0002140835,0.0001340282,0.0004231053,0.0002390145,0.00003746133,0.0003781905],"genre_scores_gemma":[0.1582106,0.00003006762,0.8413119,0.0002043816,0.00008748273,0.00007278541,0.00003134325,0.00001902709,0.00003241182],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1530554,"threshold_uncertainty_score":0.9976076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06358645407001984,"score_gpt":0.4203984667152716,"score_spread":0.3568120126452518,"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."}}