{"id":"W4242723469","doi":"10.22215/etd/2014-10496","title":"Conditional Density Estimation and Density Forecast With Applications","year":2014,"lang":"en","type":"dissertation","venue":"","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"University of Ottawa","keywords":"Univariate; Multivariate statistics; Econometrics; Conditional probability distribution; Statistics; Stock (firearms); Multivariate kernel density estimation; Conditional expectation; Wind speed; Estimation; Mathematics; Conditional variance; Meteorology; Computer science; Geography; Economics; Autoregressive conditional heteroskedasticity; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005225941,0.001286793,0.0012298,0.002198963,0.0006014601,0.00158574,0.001496538,0.001707824,0.006397547],"category_scores_gemma":[0.03343126,0.0008606034,0.001520628,0.002883834,0.001488707,0.002939388,0.001945951,0.0036867,0.001626304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001124305,"about_ca_system_score_gemma":0.001204856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008123475,"about_ca_topic_score_gemma":0.00343827,"domain_scores_codex":[0.9980288,0.001100907,0.00008543905,0.0003163454,0.0003867213,0.00008179955],"domain_scores_gemma":[0.9865971,0.01055442,0.0004620611,0.0006636847,0.001579816,0.0001429517],"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":[0.00005478842,0.00005616739,0.001765117,0.0002611912,0.00009838684,0.0001331613,0.0002577233,0.3142869,0.0006289238,0.4997491,0.007992614,0.174716],"study_design_scores_gemma":[0.000009452701,0.00001638191,0.0003323756,0.00007651765,0.00002219834,0.00007284909,0.00003178965,0.8318408,0.0004996778,0.1599116,0.007155722,0.0000307162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00109479,0.001137191,0.9954917,0.0002692147,0.00008511724,0.00001807052,0.00006412718,0.0001770094,0.001662896],"genre_scores_gemma":[0.1694492,0.01125166,0.8035661,0.000370251,0.001219603,0.0003889615,0.001003628,0.0004317574,0.01231887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008123475,"threshold_uncertainty_score":0.02763778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01767640500191826,"score_gpt":0.2214214748969392,"score_spread":0.2037450698950209,"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."}}