{"id":"W2612728037","doi":"","title":"MAXIMUM LIKELIHOOD INFERENCE IN ROBUST LINEAR MIXED-EFFECTS MODELS USING MULTIVARIATE t DISTRIBUTIONS","year":2007,"lang":"en","type":"article","venue":"Statistica Sinica","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Akaike information criterion; Outlier; Restricted maximum likelihood; Estimator; Random effects model; Multivariate statistics; Generalized linear mixed model; Mathematics; Mixed model; Maximum likelihood; Statistics; Inference; Expectation–maximization algorithm; Bayesian information criterion; Degrees of freedom (physics and chemistry); Maximum likelihood sequence estimation; Linear model; M-estimator; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.03081756,0.002347456,0.003644084,0.003627403,0.0009651653,0.002608918,0.00389456,0.002845249,0.002998496],"category_scores_gemma":[0.1278404,0.001617887,0.003735115,0.004248259,0.003026497,0.003502694,0.003330068,0.003629699,0.000948291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001579704,"about_ca_system_score_gemma":0.002514855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004186373,"about_ca_topic_score_gemma":0.003761143,"domain_scores_codex":[0.9772703,0.01942592,0.0005962045,0.001345613,0.001130908,0.0002310536],"domain_scores_gemma":[0.9072016,0.08681141,0.002823879,0.001847389,0.001079659,0.0002360235],"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":[0.000226862,0.00009740693,0.001350214,0.0007166719,0.0007172996,0.0003311698,0.0003625532,0.5915732,0.0007669171,0.2798619,0.001906974,0.1220888],"study_design_scores_gemma":[0.00005829127,0.00004462728,0.0002152089,0.00007120756,0.0000523601,0.00005850669,0.00003170448,0.730365,0.0004165883,0.2675628,0.001089544,0.00003412935],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007776585,0.000186288,0.9986839,0.00006436031,0.000008636867,0.00002159397,0.00002399572,0.00009859658,0.0001348826],"genre_scores_gemma":[0.05938783,0.000889121,0.9378976,0.0001260522,0.0001051249,0.000521286,0.0002534536,0.0001917591,0.0006278071],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03081756,"threshold_uncertainty_score":0.1629809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1199205831467778,"score_gpt":0.41746524140848,"score_spread":0.2975446582617022,"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."}}