{"id":"W2013623941","doi":"10.1002/cjs.5550360206","title":"Local influence in multilevel models","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Multilevel model; Computation; Measure (data warehouse); Random effects model; Econometrics; Mathematics; Simple (philosophy); Statistics; Regression analysis; Matrix (chemical analysis); Regression; Computer science; Algorithm; Data mining","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.01549345,0.0009540856,0.002667471,0.002298926,0.001606291,0.002612613,0.002286975,0.002079596,0.00404837],"category_scores_gemma":[0.0737555,0.0009545507,0.003070448,0.00223615,0.004289579,0.002875004,0.005063889,0.003443854,0.00042078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002531263,"about_ca_system_score_gemma":0.0009779106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007961302,"about_ca_topic_score_gemma":0.007786554,"domain_scores_codex":[0.9801618,0.01455863,0.0003825484,0.002139185,0.001993845,0.0007639868],"domain_scores_gemma":[0.8990806,0.08514643,0.006353257,0.0055771,0.002747616,0.001095013],"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":[0.0002584833,0.00009837698,0.01694739,0.0004332585,0.001189212,0.001034571,0.002058263,0.4352576,0.001724617,0.5001972,0.001888188,0.03891284],"study_design_scores_gemma":[0.00003634756,0.0001365415,0.004516008,0.000104805,0.0002802158,0.0001733245,0.000224641,0.6246703,0.0006929046,0.3667389,0.002369431,0.00005655018],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08832409,0.001448484,0.9016031,0.001048908,0.00006309998,0.00007514232,0.0001181992,0.000201398,0.007117562],"genre_scores_gemma":[0.9433809,0.0007155052,0.0523612,0.0002337318,0.0001482364,0.0001462412,0.0001095377,0.0001122772,0.002792514],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01549345,"threshold_uncertainty_score":0.08193821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09810041417147447,"score_gpt":0.3278599594412723,"score_spread":0.2297595452697978,"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."}}