{"id":"W1983074539","doi":"10.2307/3315967","title":"Kendall's tau for serial dependence","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation Francqui - Stichting","keywords":"Nonparametric statistics; Univariate; Mathematics; Statistics; Null hypothesis; Autocorrelation; Autoregressive model; Statistic; Monte Carlo method; Asymptotic distribution; Context (archaeology); Series (stratigraphy); Independence (probability theory); Null (SQL); Econometrics; Parametric statistics; Statistical hypothesis testing; Applied mathematics; Estimator; Multivariate statistics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003785377,0.0000809647,0.0002581213,0.0001398701,0.000133236,0.00006613824,0.0001804373,0.00006640354,0.001246667],"category_scores_gemma":[0.0003146559,0.0000982793,0.00006533397,0.00007488215,0.00004428977,0.0001378849,0.000002305112,0.0001292984,0.00006987387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001150017,"about_ca_system_score_gemma":0.0004228993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005040259,"about_ca_topic_score_gemma":0.01677427,"domain_scores_codex":[0.9990189,0.000005488484,0.0005717289,0.0001116671,0.00002667577,0.0002655342],"domain_scores_gemma":[0.9992132,0.00005741441,0.0002162156,0.00009937418,0.0001101841,0.000303596],"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":[0.0001781991,0.00004561393,0.03775524,0.00009060546,0.0000965904,0.0001554599,0.002216299,0.005538615,0.000009418924,0.762678,0.0592956,0.1319403],"study_design_scores_gemma":[0.001257054,0.0003848705,0.01893356,0.00004619005,0.00002230071,0.0000575055,0.00007131299,0.02214611,0.00002223468,0.4142931,0.5423787,0.0003870661],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2840751,0.002363754,0.6978638,0.0005946478,0.002128326,0.0002486046,0.007613715,0.000007126504,0.005105029],"genre_scores_gemma":[0.9471067,0.0001659835,0.0511966,0.0001745996,0.0004147929,0.000001958423,0.00001419633,0.00001883876,0.0009063343],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6630316,"threshold_uncertainty_score":0.9996663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03796314985015479,"score_gpt":0.217070838835212,"score_spread":0.1791076889850572,"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."}}