{"id":"W2015220578","doi":"10.1002/cjs.10131","title":"Pair‐copula constructions for non‐Gaussian DAG models","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Copula (linguistics); Univariate; Bivariate analysis; Conditional independence; Econometrics; Gaussian; Mathematics; Multivariate statistics; Directed acyclic graph; Conditional dependence; Statistical model; Statistics; Applied mathematics; Computer science; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000655628,0.0001029415,0.0003335352,0.0002770429,0.0002006896,0.00005502295,0.0001464531,0.00008229759,0.0001550469],"category_scores_gemma":[0.0003547311,0.0001184525,0.00009796677,0.0001027683,0.00007375151,0.0003262157,0.000005084921,0.0001701838,0.00002786152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001655998,"about_ca_system_score_gemma":0.0003524069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002373447,"about_ca_topic_score_gemma":0.003843149,"domain_scores_codex":[0.9986801,0.000008061274,0.0007874321,0.00009979816,0.00003132967,0.0003932917],"domain_scores_gemma":[0.9986262,0.00007124022,0.0004442689,0.0001429636,0.0001533383,0.0005619547],"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":[0.000007757563,0.00001786594,0.07916819,0.00002648592,0.00003566646,0.000005035443,0.001064368,0.001263747,0.000001233462,0.8954252,0.01869752,0.004286957],"study_design_scores_gemma":[0.0009427995,0.0001672179,0.02847581,0.00005399784,0.00004843264,0.0000886523,0.0004473338,0.1497935,0.00001263284,0.6220335,0.1974903,0.0004458183],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03531422,0.001000558,0.9556448,0.0002669526,0.002151884,0.0001223788,0.003277278,0.000002713943,0.002219227],"genre_scores_gemma":[0.909646,0.00003288775,0.0895424,0.0001059147,0.0003664686,0.000002746562,0.00001756265,0.00001860976,0.0002673766],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8743318,"threshold_uncertainty_score":0.4830352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0669293461452444,"score_gpt":0.2340779152763904,"score_spread":0.167148569131146,"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."}}