{"id":"W4286982496","doi":"10.48550/arxiv.2109.03844","title":"On a quantile autoregressive conditional duration model applied to\\n high-frequency financial data","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Quantile; Autoregressive model; Econometrics; Conditional probability distribution; Conditional expectation; Percentile; Quantile regression; Expectation–maximization algorithm; Mathematics; Statistics; Computer science; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":false,"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":[0.004294584,0.0006352091,0.00075952,0.0007873827,0.0004302518,0.001292579,0.001551696,0.001157927,0.002839871],"category_scores_gemma":[0.01152578,0.0003451165,0.0008899753,0.001993011,0.0008437756,0.00138853,0.001035677,0.001698454,0.0005264073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001016791,"about_ca_system_score_gemma":0.001451503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0234699,"about_ca_topic_score_gemma":0.01359077,"domain_scores_codex":[0.9989547,0.000578666,0.00003774895,0.0002020108,0.0001423817,0.00008452232],"domain_scores_gemma":[0.9952992,0.003568267,0.000355492,0.0002995262,0.0003929732,0.00008457117],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000565861,0.00004084756,0.003010941,0.00006944257,0.00004161077,0.0001604932,0.0001251986,0.8708956,0.0006759993,0.08909005,0.001351196,0.03448204],"study_design_scores_gemma":[0.000001982592,0.000007923623,0.0002907666,0.000005064024,0.000004572094,0.000012194,0.000007173144,0.9944904,0.00005766051,0.004877989,0.0002394109,0.000004857],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03607799,0.0003960588,0.9607503,0.0004592544,0.00005007146,0.00005541348,0.0002132758,0.0002901045,0.001707553],"genre_scores_gemma":[0.8136754,0.002037195,0.1733414,0.000288928,0.0002144094,0.0002583222,0.001340349,0.0001883606,0.008655678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0234699,"threshold_uncertainty_score":0.04666656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2238297376117218,"score_gpt":0.2857820901808907,"score_spread":0.06195235256916887,"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."}}