{"id":"W4230320142","doi":"10.1007/s10463-009-0239-z","title":"Semiparametric marginal and association regression methods for clustered binary data","year":2009,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; University of Waterloo","funders":"National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Science Foundation","keywords":"Inference; Semiparametric regression; Nuisance parameter; Marginal model; Mathematics; Econometrics; Binary data; Statistics; Association (psychology); Binary number; Semiparametric model; Regression; Statistical inference; Regression analysis; Computer science; Artificial intelligence; Estimator; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0231125,0.001388172,0.003582816,0.003325828,0.001163704,0.003361663,0.00615452,0.002634725,0.005943621],"category_scores_gemma":[0.0819649,0.001886287,0.003553811,0.005173301,0.003717381,0.005588094,0.005994761,0.005969769,0.001648901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001405129,"about_ca_system_score_gemma":0.002651694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002331348,"about_ca_topic_score_gemma":0.002609204,"domain_scores_codex":[0.9848669,0.01055315,0.0005322841,0.001955492,0.001694534,0.000397609],"domain_scores_gemma":[0.9363751,0.04830372,0.003370146,0.007587198,0.003567165,0.0007966709],"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":[0.0002968434,0.0001873454,0.003467228,0.0004288568,0.0005340014,0.000122211,0.0005340183,0.1173838,0.001084455,0.7253205,0.004064722,0.1465761],"study_design_scores_gemma":[0.00003860187,0.00004113359,0.001075709,0.00006037494,0.00009383661,0.0001359457,0.00005023861,0.5105639,0.0003238025,0.4847136,0.002846886,0.00005597274],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002488407,0.0003422371,0.9965389,0.0001838332,0.00002494618,0.00001319484,0.00005633655,0.00009436766,0.0002577669],"genre_scores_gemma":[0.1835211,0.00186679,0.8051949,0.0003909762,0.0005834438,0.0005931112,0.001073889,0.0004462781,0.006329555],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0231125,"threshold_uncertainty_score":0.1222321,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1370268744800424,"score_gpt":0.4420097317015558,"score_spread":0.3049828572215134,"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."}}