{"id":"W2951104656","doi":"10.1002/cjs.11726","title":"The EAS approach for graphical selection consistency in vector autoregression models","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian vector autoregression; Model selection; Vector autoregression; Frequentist inference; Consistency (knowledge bases); Pairwise comparison; Inference; Econometrics; Bayesian probability; Selection (genetic algorithm); Autoregressive model; Bayesian inference; Computer science; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001576839,0.00009777695,0.0002424387,0.0001550751,0.0005418487,0.00005563897,0.0002272939,0.0000401757,0.00008206427],"category_scores_gemma":[0.003578169,0.00007096447,0.00005893646,0.0002296422,0.0001305707,0.00003771606,0.00001355764,0.0004275038,1.645729e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002338081,"about_ca_system_score_gemma":0.00113139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000476701,"about_ca_topic_score_gemma":0.002519341,"domain_scores_codex":[0.9984356,0.0003113059,0.0005940113,0.000107295,0.0002679084,0.0002838456],"domain_scores_gemma":[0.996749,0.002271886,0.0003056422,0.0001097724,0.000292861,0.0002707811],"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":[0.00003935521,0.0000265314,0.0004438618,0.00002614468,0.00001717481,0.00002150124,0.0002553402,0.00044565,0.000008687146,0.9711825,0.02074786,0.006785435],"study_design_scores_gemma":[0.0003561822,0.0002742141,0.00101979,0.00001644549,0.00003092062,0.00009078649,0.0004125145,0.1504479,0.000004953,0.8448426,0.002406919,0.00009671062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002483172,0.0001458521,0.9955255,0.0002456712,0.0003677527,0.0002132492,0.0006452991,0.000003140284,0.0003703848],"genre_scores_gemma":[0.3596871,0.00000815022,0.6400551,0.00006195793,0.00006028226,0.00002681383,0.000007097194,0.00001652666,0.00007692802],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.357204,"threshold_uncertainty_score":0.4283661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1110397452936664,"score_gpt":0.3228062879368412,"score_spread":0.2117665426431749,"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."}}