{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001310797,0.0001151097,0.0002323381,0.00009437877,0.0001768443,0.00005380029,0.0007119774,0.00004267674,0.0001964339],"category_scores_gemma":[0.007097056,0.00006591556,0.00002886183,0.000230864,0.0001177718,0.00004212649,0.00003452997,0.0002543735,0.00001406779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006381373,"about_ca_system_score_gemma":0.0005046241,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001785638,"about_ca_topic_score_gemma":0.0356688,"domain_scores_codex":[0.9986122,0.0002225462,0.0005441416,0.0001284148,0.0002442401,0.0002484276],"domain_scores_gemma":[0.9944492,0.003711292,0.0003570676,0.0007548346,0.0003475048,0.0003801033],"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":[0.00002583846,0.00003568247,0.00007603495,0.00002129895,0.00003115032,0.0006423863,0.0004191977,0.000003001169,0.000008517501,0.7905738,0.04165262,0.1665105],"study_design_scores_gemma":[0.000222879,0.0003749904,0.000776048,0.00007673634,0.0001185844,0.001689823,0.0007207417,0.005166503,0.0000190163,0.9825137,0.008196925,0.0001240266],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002751983,0.00002588761,0.9922555,0.002339157,0.0001666209,0.0002369243,0.001807475,0.000002419574,0.000414027],"genre_scores_gemma":[0.4687373,0.000009326303,0.5305468,0.0005367986,0.0001190732,0.000004165936,0.000006475294,0.00001670468,0.00002340081],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4659853,"threshold_uncertainty_score":0.9819278,"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."}}