{"id":"W2084235223","doi":"10.1016/j.csda.2012.01.011","title":"Bootstrap testing multiple changes in persistence for a heavy-tailed sequence","year":2012,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":25,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Test statistic; Null hypothesis; Mathematics; Null (SQL); Null distribution; Statistics; Econometrics; Alternative hypothesis; Statistical hypothesis testing; Sequence (biology); Sequential analysis; Score test; Statistic; Computer science; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008861427,0.0001802448,0.0006115527,0.0005560319,0.0001956538,0.0001198871,0.0004256014,0.00005041763,0.0003222627],"category_scores_gemma":[0.0008602901,0.0002155266,0.0001116136,0.001533723,0.00006309401,0.000332461,0.000166517,0.00008648395,0.00006829613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001192707,"about_ca_system_score_gemma":0.00003133853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002345669,"about_ca_topic_score_gemma":0.003644802,"domain_scores_codex":[0.9981659,0.00002960757,0.000718259,0.0005674802,0.00009817602,0.0004205644],"domain_scores_gemma":[0.9978898,0.0008429982,0.0004495571,0.000544795,0.0001443267,0.0001285465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001984534,0.0001396991,0.8755537,0.00007513303,0.000966997,0.000003053911,0.0003242589,0.05374065,0.000004710755,0.06626226,0.0007725721,0.002137076],"study_design_scores_gemma":[0.0002647061,0.00002398214,0.1490777,0.000008861685,0.0002224755,0.00000171919,0.00008775231,0.8377279,6.000636e-7,0.007622289,0.004719941,0.0002420588],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0133087,0.0008880961,0.9629824,0.0002237868,0.0001009906,0.0002342259,0.02201592,0.00002641716,0.0002195226],"genre_scores_gemma":[0.8177645,0.00001662679,0.1758642,0.00007833482,0.0001050692,0.0000360818,0.00600148,0.00001535426,0.0001184094],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8044558,"threshold_uncertainty_score":0.8788921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3345250071365134,"score_gpt":0.3234650715854638,"score_spread":0.01105993555104956,"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."}}