{"id":"W1992410926","doi":"10.1002/cjs.10055","title":"Inferences in generalized linear longitudinal mixed models","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Generalized estimating equation; Covariate; Statistics; Generalized linear model; Generalization; Correlation; Generalized linear mixed model; Moment (physics); Contrast (vision); Econometrics; Mixed model; Binary data; Regression; Regression analysis; Estimating equations; Linear model; Binary number; Maximum likelihood; Mathematical analysis; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000999706,0.0001613578,0.0004585542,0.0003128211,0.00007645916,0.00008008994,0.0003222489,0.0001172579,0.0008316032],"category_scores_gemma":[0.00451763,0.0001352981,0.00005481884,0.0002028559,0.000193242,0.0001302821,0.00001192036,0.0006626376,0.000008178816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005455343,"about_ca_system_score_gemma":0.001315651,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002571259,"about_ca_topic_score_gemma":0.1091146,"domain_scores_codex":[0.9982406,0.0001434681,0.0008506742,0.0001360727,0.0002616664,0.0003674476],"domain_scores_gemma":[0.9971724,0.001176657,0.000352122,0.0001982579,0.0004714133,0.0006291321],"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":[0.00001115853,0.00002117482,0.008713892,0.00002748141,0.00001822212,0.0004298298,0.0002435981,0.00001943851,0.00006930938,0.9724355,0.003961693,0.01404866],"study_design_scores_gemma":[0.0004250767,0.00009030446,0.01038156,0.00005234038,0.00003223557,0.0001127517,0.00005650929,0.007386342,0.0000718879,0.9806026,0.0006213193,0.0001670413],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07498278,0.00002810154,0.9226454,0.000161322,0.0008104233,0.00007539401,0.0003710226,0.000003718092,0.0009218443],"genre_scores_gemma":[0.3440809,0.000008283117,0.6557003,0.00003645928,0.0001241015,0.000001305098,0.000002758613,0.00001273468,0.00003312168],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2690982,"threshold_uncertainty_score":0.9105472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1023048184563324,"score_gpt":0.3490306239748175,"score_spread":0.2467258055184852,"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."}}