{"id":"W2086638187","doi":"10.1080/00949650903268023","title":"Robust quasi-likelihood inference in generalized linear mixed models with outliers","year":2010,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outlier; Mathematics; Generalized linear mixed model; Consistency (knowledge bases); Generalized linear model; Inference; Statistics; Applied mathematics; Binary number; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.03857647,0.001355948,0.002945472,0.002170349,0.0008931856,0.002511318,0.004506223,0.002719915,0.00168115],"category_scores_gemma":[0.1223858,0.001593961,0.002508028,0.003006666,0.00362114,0.003459229,0.003791142,0.003416623,0.0005158391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001515912,"about_ca_system_score_gemma":0.002191855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005462359,"about_ca_topic_score_gemma":0.004347673,"domain_scores_codex":[0.9645457,0.03107615,0.0007267172,0.001581002,0.001651939,0.0004184552],"domain_scores_gemma":[0.8844551,0.1024464,0.005358687,0.005088165,0.002257582,0.0003940803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003023417,0.00007326536,0.002887161,0.0004249396,0.0006023248,0.0003780589,0.0003964932,0.7008541,0.0007899306,0.2298695,0.00114913,0.06227271],"study_design_scores_gemma":[0.000041391,0.00003962494,0.0002706352,0.00002334118,0.00002777212,0.00004559333,0.00002530077,0.8937373,0.0002841623,0.1049577,0.0005227852,0.00002451653],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002803212,0.0001797855,0.9966307,0.0001135703,0.00001325473,0.00002041258,0.00003124878,0.00009953274,0.0001081936],"genre_scores_gemma":[0.1767865,0.0005265748,0.8207785,0.0002176001,0.0001136173,0.0003796323,0.0003595345,0.0001909505,0.0006471134],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03857647,"threshold_uncertainty_score":0.2040143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1279825903358278,"score_gpt":0.4191360804875858,"score_spread":0.291153490151758,"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."}}