{"id":"W2074965526","doi":"10.2307/3316052","title":"On the application of extended quasi‐likelihood to the clustered data case","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Maximum likelihood; Quasi-maximum likelihood; Mathematics; Statistics; Quasi-likelihood; Estimating equations; Maximum likelihood sequence estimation; Sample size determination; Mean squared error; Restricted maximum likelihood; Generalized estimating equation; Likelihood function; Applied mathematics; Count data; Poisson distribution","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":[0.02507976,0.0005204481,0.001173231,0.001241044,0.0005459256,0.001682911,0.002255997,0.001575771,0.003433766],"category_scores_gemma":[0.1215289,0.0005675931,0.001082739,0.002464684,0.003236388,0.002832209,0.002765356,0.002557672,0.0003252023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001107574,"about_ca_system_score_gemma":0.001226203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004555672,"about_ca_topic_score_gemma":0.002731485,"domain_scores_codex":[0.9768969,0.02041465,0.00034764,0.000767125,0.001305163,0.0002685761],"domain_scores_gemma":[0.8610854,0.1256491,0.003724726,0.0055478,0.003451246,0.0005417964],"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":[0.0001282849,0.00004792989,0.003002223,0.000197044,0.0002035082,0.0005098939,0.0007185772,0.1903554,0.0003286949,0.7365997,0.001749142,0.06615959],"study_design_scores_gemma":[0.00003995834,0.00006748468,0.001081718,0.00007371259,0.00003923405,0.0001225582,0.0001198744,0.5014697,0.000259725,0.4943113,0.002382093,0.00003277365],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01375707,0.0005159333,0.9826302,0.0007949851,0.00005011169,0.00004444055,0.00004084516,0.00005445765,0.002111939],"genre_scores_gemma":[0.4688306,0.001368938,0.5253693,0.0007808001,0.0002543734,0.0002775188,0.0001558348,0.0001305159,0.0028321],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02507976,"threshold_uncertainty_score":0.1326361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.101143858658377,"score_gpt":0.3643984108449334,"score_spread":0.2632545521865564,"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."}}