{"id":"W2952412542","doi":"10.1017/asb.2016.22","title":"A FORM OF MULTIVARIATE PARETO DISTRIBUTION WITH APPLICATIONS TO FINANCIAL RISK MEASUREMENT","year":2016,"lang":"en","type":"preprint","venue":"Astin Bulletin","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Government of Ontario","keywords":"Pareto distribution; Joint probability distribution; Heavy-tailed distribution; Generalized Pareto distribution; Univariate; Maxima and minima; Mathematics; Lebesgue measure; Pareto principle; Multivariate statistics; Distribution (mathematics); Marginal distribution; Lomax distribution; Maxima; Measure (data warehouse); Econometrics; Multivariate normal distribution; Probability distribution; Applied mathematics; Statistics; Extreme value theory; Lebesgue integration; Random variable; Computer science; Discrete mathematics; Mathematical analysis; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00119868,0.0002961701,0.0006448021,0.000120365,0.0001429447,0.00003509767,0.0003491002,0.0002605048,0.0001006056],"category_scores_gemma":[0.0007950767,0.000282144,0.0001640295,0.0001493459,0.00005512706,0.00002553388,0.0003112734,0.0003612049,0.0003317236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002962321,"about_ca_system_score_gemma":0.000126173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001425479,"about_ca_topic_score_gemma":0.0001718038,"domain_scores_codex":[0.9977851,0.00002502259,0.0009455637,0.0007518802,0.0001314795,0.0003609742],"domain_scores_gemma":[0.9979821,0.00005933861,0.0008849123,0.0006574251,0.000298326,0.0001178466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001757027,0.001434759,0.2750295,0.001501546,0.000461488,0.000006579306,0.003283842,0.009096446,0.0001524883,0.5805254,0.02263887,0.1041121],"study_design_scores_gemma":[0.001632719,0.0003253453,0.1949196,0.0009936634,0.00008200036,0.00000125875,0.00002384567,0.00315963,0.0003621654,0.1644167,0.6328269,0.001256199],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05543244,0.0005212756,0.9360937,0.001054653,0.0002130265,0.001248307,0.003983963,0.0000508712,0.001401757],"genre_scores_gemma":[0.990131,0.00009815115,0.008531963,0.00004392362,0.0002353496,0.0006954409,0.0001189103,0.00003490784,0.0001103798],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9346985,"threshold_uncertainty_score":0.999963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04069214233499126,"score_gpt":0.2314296528169641,"score_spread":0.1907375104819728,"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."}}