{"id":"W3011772116","doi":"10.1002/cjs.11543","title":"A semiparametric stochastic mixed effects model for bivariate cyclic longitudinal data","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bivariate analysis; Nonparametric statistics; Mathematics; Estimator; Smoothing; Smoothing spline; Random effects model; Mixed model; Statistics; Parametric statistics; Parametric model; Joint probability distribution; Autocorrelation; Semiparametric regression; Econometrics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0006205617,0.0002034345,0.0005449301,0.0002076324,0.0001306192,0.000102068,0.0006836365,0.00008561737,0.00005154571],"category_scores_gemma":[0.04015613,0.0001847176,0.00005874698,0.0003376003,0.0001093917,0.0001062981,0.00004770472,0.0003303588,0.000007892635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000909273,"about_ca_system_score_gemma":0.001178922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001987672,"about_ca_topic_score_gemma":0.0007650795,"domain_scores_codex":[0.9982723,0.0000974482,0.0006691467,0.0002510358,0.0002611332,0.0004489491],"domain_scores_gemma":[0.9922601,0.005270643,0.000417541,0.0003375282,0.000426672,0.00128753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001436789,0.0000702099,0.0004161526,0.001114557,0.0003522217,0.0004730755,0.001006846,0.003572691,0.00009812238,0.8099678,0.1418666,0.04091812],"study_design_scores_gemma":[0.0006400647,0.0003198683,0.0004716116,0.00009023703,0.0003041747,0.00003866135,0.00002499594,0.629225,0.00001379434,0.3685224,0.0001609952,0.0001882818],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001714397,0.0001506947,0.9925309,0.0003455893,0.0003670526,0.0002744461,0.004564113,0.000008754905,0.00004400079],"genre_scores_gemma":[0.3055383,0.000004870692,0.6939902,0.0002211096,0.0001670948,0.000003769004,0.00002359028,0.00003109597,0.00001998773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6256523,"threshold_uncertainty_score":0.9679291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3606170897567736,"score_gpt":0.3692856778476681,"score_spread":0.008668588090894491,"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."}}