{"id":"W2096274285","doi":"10.2307/3315854","title":"On quasi‐likelihood inference in generalized linear mixed models with two components of dispersion","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Mathematics; Statistics; Estimator; Quasi-maximum likelihood; Generalized linear model; Dispersion (optics); Variance components; Generalized linear mixed model; Maximum likelihood; Mixed model; Applied mathematics; Likelihood function; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.05296685,0.001182851,0.002753376,0.00251264,0.001074193,0.003182145,0.004261813,0.0028331,0.003002558],"category_scores_gemma":[0.1743961,0.001762982,0.002401893,0.003158135,0.00604087,0.004412765,0.005016347,0.003773183,0.0004456067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001938299,"about_ca_system_score_gemma":0.002392714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006383848,"about_ca_topic_score_gemma":0.00447744,"domain_scores_codex":[0.9594039,0.03686959,0.0006450635,0.001068897,0.001588295,0.0004243285],"domain_scores_gemma":[0.7395434,0.2459703,0.004235852,0.006265164,0.003271117,0.0007142968],"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":[0.0001922442,0.0000784247,0.002862007,0.0002350809,0.0003637547,0.0002429304,0.0005434818,0.2232846,0.0005395911,0.7278406,0.001285888,0.04253145],"study_design_scores_gemma":[0.00004924408,0.00004233764,0.0004423923,0.0000469596,0.00003090024,0.0000531412,0.00003800794,0.6783046,0.0001963446,0.3197876,0.0009729248,0.00003555469],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003490967,0.0001860359,0.9957036,0.0002527102,0.00002808577,0.00002045705,0.00002472434,0.00005108236,0.0002423211],"genre_scores_gemma":[0.2203415,0.0008398802,0.775015,0.000525896,0.0003292173,0.0006165266,0.0002803743,0.0002016376,0.001849925],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05296685,"threshold_uncertainty_score":0.2801188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.103847777655825,"score_gpt":0.3234960585517568,"score_spread":0.2196482808959318,"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."}}