{"id":"W1991705432","doi":"10.1002/cjs.5550350110","title":"Marginalized transition random effect models for multivariate longitudinal binary data","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multivariate statistics; Marginal model; Random effects model; Covariate; Statistics; Binary data; Markov chain Monte Carlo; Logistic regression; Mathematics; Econometrics; Markov chain; Generalized linear mixed model; Binary number; Regression analysis; Computer science; Monte Carlo method","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02913236,0.001566752,0.002667455,0.00291381,0.0007396247,0.002241455,0.004672189,0.00219625,0.009621086],"category_scores_gemma":[0.06335305,0.001181969,0.003385426,0.003042103,0.002823248,0.00364394,0.002729963,0.004964656,0.001308991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002293744,"about_ca_system_score_gemma":0.001934326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009852592,"about_ca_topic_score_gemma":0.007219689,"domain_scores_codex":[0.9845916,0.01187863,0.0004305823,0.001664573,0.0009715707,0.0004630054],"domain_scores_gemma":[0.9431395,0.04644043,0.003611988,0.003655738,0.00239078,0.0007617015],"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":[0.0003806892,0.0001452869,0.005634192,0.000296513,0.0005103656,0.0004137109,0.0007591008,0.1877288,0.0004765977,0.7612311,0.004996531,0.03742702],"study_design_scores_gemma":[0.00008707403,0.00009268946,0.001309971,0.00008939961,0.0001157157,0.0001079668,0.00006881871,0.5092739,0.0001671073,0.4851015,0.003528944,0.00005677975],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0107842,0.0006389354,0.9859501,0.0006132358,0.00008723111,0.0001248942,0.0006407227,0.0003704765,0.0007902781],"genre_scores_gemma":[0.4243793,0.001814715,0.5550471,0.0006040717,0.0003812791,0.002419474,0.002825234,0.0003754059,0.01215327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02913236,"threshold_uncertainty_score":0.1540685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1660987715882368,"score_gpt":0.3774538329787158,"score_spread":0.211355061390479,"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."}}