{"id":"W1669348267","doi":"10.1016/j.jeconom.2015.11.001","title":"Bayesian semiparametric modeling of realized covariance matrices","year":2015,"lang":"en","type":"article","venue":"Journal of Econometrics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Wishart distribution; Inverse-Wishart distribution; Covariance; Mathematics; Bayesian probability; Posterior probability; Econometrics; Conditional probability distribution; Mixture model; Inverse; Applied mathematics; Statistics; Multivariate statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003372361,0.0001410202,0.0005657359,0.002284903,0.00003152476,0.0001145756,0.00113218,0.0001075215,0.000007074686],"category_scores_gemma":[0.001205983,0.0001208236,0.0001917736,0.00421557,0.00002310813,0.0009495177,0.0001287601,0.0002475847,0.000003583893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000114353,"about_ca_system_score_gemma":0.0003296864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001793384,"about_ca_topic_score_gemma":5.871817e-7,"domain_scores_codex":[0.9980443,0.0001345072,0.001010207,0.000199896,0.0003725984,0.0002385345],"domain_scores_gemma":[0.9971502,0.0003449749,0.001115879,0.0004135708,0.0006577878,0.0003176073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002431462,0.0008652063,0.004437195,0.0002585736,0.0004817384,0.0002111345,0.002572772,0.2236412,0.0001596882,0.300934,0.008186299,0.458009],"study_design_scores_gemma":[0.001067488,0.0003306969,0.00006056001,0.00004096033,0.00003386429,0.000178048,0.00003334308,0.8900466,0.0002199901,0.1064118,0.001384585,0.0001920816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004055251,0.004944001,0.9860616,0.0002636914,0.0007198677,0.00006592064,0.000002755117,0.00001376445,0.003873118],"genre_scores_gemma":[0.3722998,0.0004130774,0.6270565,0.00007605382,0.0001038658,5.504386e-7,1.901623e-7,0.000007740522,0.00004220359],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6664054,"threshold_uncertainty_score":0.4927045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1888874534378196,"score_gpt":0.2982408457396074,"score_spread":0.1093533923017878,"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."}}