{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02063571,0.001970735,0.003427328,0.004187498,0.0008386888,0.003120946,0.004977643,0.002989413,0.005271178],"category_scores_gemma":[0.05873777,0.001943328,0.004121578,0.004586371,0.001954762,0.004361494,0.003739511,0.003989875,0.001541427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002757657,"about_ca_system_score_gemma":0.002519034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01995727,"about_ca_topic_score_gemma":0.01893893,"domain_scores_codex":[0.9855495,0.009170133,0.0007670534,0.002603189,0.001471563,0.0004385634],"domain_scores_gemma":[0.9709213,0.02220089,0.002245689,0.002051612,0.002371171,0.0002092479],"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":[0.000108822,0.00006735433,0.004812138,0.0004348534,0.00086884,0.0002868112,0.0006265066,0.4189949,0.001128015,0.4514494,0.004233757,0.1169887],"study_design_scores_gemma":[0.00003439484,0.00003668099,0.001584251,0.0001047281,0.0001866545,0.000101409,0.00005234493,0.7814517,0.0003409928,0.2102184,0.005802267,0.00008626295],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00329446,0.0004319702,0.9950013,0.0001973427,0.00003510313,0.00009897768,0.0003174217,0.0001717543,0.0004517128],"genre_scores_gemma":[0.117792,0.002198866,0.8686522,0.0002643451,0.0002206621,0.001268902,0.002550258,0.0002058853,0.006846965],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02063571,"threshold_uncertainty_score":0.1091334,"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."}}