{"id":"W1973761476","doi":"10.1111/j.1541-0420.2008.01058.x","title":"Joint Regression Analysis of Correlated Data Using Gaussian Copulas","year":2008,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":204,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Univariate; Copula (linguistics); Mathematics; Joint probability distribution; Regression analysis; Statistics; Generalized linear model; Logistic regression; Inference; Marginal model; Gaussian; Estimating equations; Multivariate statistics; Econometrics; Computer science; Estimator; Artificial intelligence","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":[0.0005762238,0.0001268468,0.0005241347,0.002118256,0.00009061584,0.00001267645,0.0003298335,0.0001202859,0.0002206589],"category_scores_gemma":[0.006313239,0.00009508031,0.00008610574,0.01206245,0.0001246714,0.00007185654,0.0002147341,0.0001086833,0.000005219754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004266105,"about_ca_system_score_gemma":0.00005508879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001078177,"about_ca_topic_score_gemma":0.000002847751,"domain_scores_codex":[0.998472,0.0001216731,0.0005209513,0.0002851976,0.0004117307,0.0001884831],"domain_scores_gemma":[0.9975913,0.0009672343,0.0003221898,0.0008750946,0.0001416871,0.0001025052],"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":[0.0002326592,0.002817118,0.321124,0.001116226,0.006350444,0.0007462059,0.001586786,0.0000367331,0.04123376,0.3344223,0.02802678,0.2623071],"study_design_scores_gemma":[0.00130152,0.0003055566,0.2814996,0.000388419,0.006327394,0.00008819049,0.0001726371,0.6189803,0.004816142,0.08411794,0.0009521362,0.001050131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.101901,0.0001497561,0.8968605,0.00001472746,0.000152347,0.00008272076,0.000291941,0.00003159835,0.0005154512],"genre_scores_gemma":[0.2951652,0.00006509631,0.7046301,0.00001005091,0.0000179822,5.318666e-7,0.00005223657,0.00001136068,0.00004740802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6189436,"threshold_uncertainty_score":0.7557993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4876392554906313,"score_gpt":0.460364406222413,"score_spread":0.02727484926821827,"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."}}