{"id":"W2042297468","doi":"10.1080/00949655.2011.569721","title":"A consistent simulation-based estimator in generalized linear mixed models","year":2011,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Asymptotic distribution; Applied mathematics; Consistency (knowledge bases); Outlier; Invariant estimator; Bias of an estimator; Consistent estimator; Parametric statistics; Efficient estimator; Weak consistency; Generalized linear mixed model; Trimmed estimator; Statistics; Minimum-variance unbiased estimator; Strong consistency; Discrete mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006376786,0.0001364809,0.0003765311,0.0001945469,0.00005945561,0.00003159032,0.00005382759,0.00007794894,0.00009031886],"category_scores_gemma":[0.002514134,0.0001152803,0.0000477024,0.0001478705,0.00008309649,0.0001575842,0.00001268658,0.0001672176,0.000001697503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004426464,"about_ca_system_score_gemma":0.00008060772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001078169,"about_ca_topic_score_gemma":0.000003643386,"domain_scores_codex":[0.9981996,0.0003035402,0.0008991739,0.0001435383,0.0003086119,0.0001454929],"domain_scores_gemma":[0.9926771,0.006322749,0.0003792779,0.00006594982,0.0004128728,0.0001420556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002786191,0.000170895,0.0005178055,0.00006791213,0.00001625755,0.00003195719,0.0003175989,0.7095798,0.00001050226,0.270811,0.00001352322,0.01818412],"study_design_scores_gemma":[0.0008648676,0.000116191,0.003239917,0.00004403444,0.00002862454,0.000002401444,0.00002495288,0.5528825,0.000008788829,0.4427135,0.00000264613,0.00007162608],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03497142,0.00001662671,0.9645442,0.00003777445,0.00009793026,0.0001574742,0.00002170471,0.00001445977,0.0001383831],"genre_scores_gemma":[0.5088847,9.49382e-7,0.4910474,0.00003756709,0.00001662213,0.0000010566,0.000003326539,0.000007256191,0.000001153592],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4739133,"threshold_uncertainty_score":0.4700993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1840252625236823,"score_gpt":0.4142985681483103,"score_spread":0.2302733056246279,"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."}}