{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02604469,0.001703528,0.00226264,0.00423695,0.00100432,0.001880076,0.002683206,0.0015439,0.003582578],"category_scores_gemma":[0.1164646,0.001000505,0.003362587,0.002789696,0.00237705,0.002614364,0.003095683,0.002748175,0.0006117136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001709539,"about_ca_system_score_gemma":0.001604967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003844978,"about_ca_topic_score_gemma":0.003013823,"domain_scores_codex":[0.9736076,0.02014748,0.0008369717,0.002042834,0.003041615,0.0003234652],"domain_scores_gemma":[0.8681608,0.1209175,0.003617344,0.00363737,0.003117974,0.0005490935],"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":[0.0001516616,0.00008239756,0.005551746,0.000545328,0.001052747,0.0004886477,0.0006021416,0.1244448,0.0009358319,0.7319121,0.002092649,0.1321401],"study_design_scores_gemma":[0.00003485787,0.0001010632,0.001154741,0.00008674381,0.0002274064,0.0001744107,0.00005437123,0.5979691,0.0007484801,0.3933662,0.006040704,0.00004209862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002116944,0.0003082155,0.9965799,0.0001168061,0.00002352703,0.00004838551,0.00004264658,0.0001117841,0.0006517418],"genre_scores_gemma":[0.2386879,0.001882026,0.7531989,0.0004709243,0.0004410665,0.001465727,0.0005995459,0.000303342,0.002950575],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02604469,"threshold_uncertainty_score":0.1377392,"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."}}