{"id":"W1968705029","doi":"10.1002/cjs.11170","title":"Generalized estimating equations for mixtures with varying concentrations","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Asymptotic distribution; Parametric statistics; Generalized estimating equation; Applied mathematics; Nonparametric statistics; Statistics; Nuisance parameter; Parametric model; Gee; Estimating equations; Covariance matrix; Distribution (mathematics); Covariance; Dispersion (optics); Mixing (physics); Mathematical analysis; Estimator; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001950805,0.00008233853,0.0001410951,0.0001077952,0.0002392623,0.000282867,0.0002825014,0.00003015802,0.00003342246],"category_scores_gemma":[0.0003038825,0.00006625763,0.00002832855,0.0001225861,0.00004641797,0.0003249355,0.000005319677,0.0001011049,0.000002039743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000524853,"about_ca_system_score_gemma":0.001183178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008877389,"about_ca_topic_score_gemma":0.0007876874,"domain_scores_codex":[0.9992542,0.00004495902,0.0002699784,0.00009060388,0.0001058601,0.0002343862],"domain_scores_gemma":[0.9983803,0.0003255637,0.0002043103,0.000129898,0.0005582526,0.0004016619],"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":[0.000001695337,0.000006537623,0.00007057781,0.0000168708,0.00004166007,0.00002980994,0.001025631,0.002760549,0.0004051843,0.907127,0.01572591,0.07278857],"study_design_scores_gemma":[0.0005900599,0.0001532695,0.00011048,0.00005821683,0.00003728238,0.00009800746,0.0000122545,0.7094969,0.0003526675,0.2879803,0.0009413769,0.0001691958],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001517177,0.0001186148,0.9979008,0.001011777,0.0003355677,0.0001702361,0.00005882624,0.000005772279,0.0002466229],"genre_scores_gemma":[0.07347494,0.000001754534,0.9259185,0.0004016936,0.0001202743,0.000009134959,0.000004861145,0.000007937007,0.00006086296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7067363,"threshold_uncertainty_score":0.2727693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03148277674408478,"score_gpt":0.2665989372402002,"score_spread":0.2351161604961154,"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."}}