{"id":"W1992676321","doi":"10.2307/3315997","title":"Nonlinear mixed‐effect models with nonignorably missing covariates","year":2004,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Covariate; Missing data; Gibbs sampling; Mixed model; Random effects model; Inference; Nonlinear system; Monte Carlo method; Statistics; Econometrics; Mathematics; Longitudinal data; Generalized linear mixed model; Computer science; Applied mathematics; Statistical physics; Artificial intelligence; Data mining; 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":[0.01620327,0.001927154,0.00267126,0.001603619,0.0009304996,0.002301957,0.004909555,0.002543977,0.007564322],"category_scores_gemma":[0.03643489,0.001285682,0.002620874,0.00242477,0.002021472,0.002421392,0.002431378,0.003065686,0.00111157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001628076,"about_ca_system_score_gemma":0.001307155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0106906,"about_ca_topic_score_gemma":0.01216756,"domain_scores_codex":[0.9929502,0.005038561,0.000270808,0.0008684579,0.0005691902,0.0003027719],"domain_scores_gemma":[0.9628166,0.03161575,0.00199672,0.001832716,0.001261084,0.0004770636],"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":[0.0009503915,0.0002688425,0.00996129,0.0006633684,0.001118306,0.001530387,0.0005604928,0.3174825,0.001049721,0.6106085,0.004761854,0.05104434],"study_design_scores_gemma":[0.0001322457,0.0001129368,0.001317062,0.0001075117,0.0002628842,0.0001661933,0.00007082682,0.7193959,0.0005840745,0.2728514,0.004947917,0.00005102423],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01887311,0.001015524,0.9752874,0.001601485,0.0001881388,0.0001064188,0.0008999659,0.0002252665,0.00180268],"genre_scores_gemma":[0.5616714,0.002210654,0.4103347,0.0007029324,0.0006541474,0.0009322435,0.001782364,0.0001932106,0.02151836],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01620327,"threshold_uncertainty_score":0.08569211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04145022479494188,"score_gpt":0.3034727824495911,"score_spread":0.2620225576546493,"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."}}