{"id":"W2914216914","doi":"10.1016/j.jmva.2019.01.001","title":"Robust maximum <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" overflow=\"scroll\" id=\"d1e2104\" altimg=\"si4.gif\"><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math>-likelihood estimation of joint mean–covariance models for longitudinal data","year":2019,"lang":"lv","type":"article","venue":"Journal of Multivariate Analysis","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China; Health Sciences Centre Foundation","keywords":"Mathematics; Estimator; Outlier; Algorithm; Covariance; Statistics; Applied mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0109372,0.001947311,0.002283717,0.002658812,0.0009202877,0.002942657,0.004528602,0.002604137,0.03992373],"category_scores_gemma":[0.05877258,0.001773523,0.002912321,0.002840795,0.001057163,0.003446426,0.004119891,0.003717136,0.02191858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001539846,"about_ca_system_score_gemma":0.004887738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007002936,"about_ca_topic_score_gemma":0.009334744,"domain_scores_codex":[0.9942989,0.003337908,0.0003346634,0.0007950799,0.001003964,0.000229532],"domain_scores_gemma":[0.9826936,0.01167927,0.001042137,0.002758978,0.001560014,0.0002660212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006254268,0.0002643403,0.001950761,0.001580558,0.0008702683,0.0002322402,0.0003073945,0.1907622,0.003802236,0.2103134,0.1118993,0.4773919],"study_design_scores_gemma":[0.0001019903,0.0001044964,0.001562046,0.0002721671,0.00009453243,0.0001473161,0.00008301704,0.7606037,0.004477044,0.1969357,0.03550096,0.0001170434],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008138969,0.0002193306,0.9930702,0.0003438971,0.00004357912,0.00008413725,0.001365639,0.00215633,0.00190303],"genre_scores_gemma":[0.0396813,0.0006174388,0.9344693,0.0002733028,0.0001775778,0.0009200206,0.008934992,0.003993635,0.01093247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03992373,"threshold_uncertainty_score":0.1335582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06080553002046117,"score_gpt":0.3120470590199192,"score_spread":0.2512415289994581,"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."}}