{"id":"W2887557474","doi":"10.1002/asmb.2373","title":"Change points in heavy‐tailed multivariate time series: Methods using precision matrices","year":2018,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Series (stratigraphy); Inference; Computer science; Multivariate normal distribution; Data point; Algorithm; Normality; Graph; Time series; Mathematics; Data mining; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001261991,0.0003055572,0.0006009704,0.0002643008,0.0001084365,0.00006335473,0.0001915007,0.0005308919,0.0001807461],"category_scores_gemma":[0.0008715226,0.0002652679,0.00002053638,0.0007949145,0.0002015496,0.0002784892,0.0002323082,0.0005106587,0.000007299229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000716842,"about_ca_system_score_gemma":0.00006204571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003303108,"about_ca_topic_score_gemma":0.00001502458,"domain_scores_codex":[0.998037,0.0001631359,0.0006086765,0.0005167734,0.0002257081,0.000448738],"domain_scores_gemma":[0.9981679,0.001068937,0.0001864131,0.000315289,0.0001500633,0.0001113977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001822196,0.0009394556,0.0003031577,0.0007615089,0.00007330169,0.00003764966,0.007978478,0.0034722,0.007550796,0.7013403,0.0001114722,0.2756094],"study_design_scores_gemma":[0.001068138,0.00004904233,0.001209001,0.0005316332,0.00002867854,0.00001192353,0.0001835256,0.4072621,0.0001691348,0.5891224,0.000009430473,0.0003550153],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1496346,0.00004655501,0.8485811,0.00008450844,0.0001328006,0.0005764799,0.00001909331,0.00003627909,0.0008885738],"genre_scores_gemma":[0.3715854,0.00000855894,0.6279687,0.00007598402,0.000179519,0.0001117751,0.00000256103,0.00003709717,0.0000303589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4037899,"threshold_uncertainty_score":0.99998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1739370695832208,"score_gpt":0.4060037721326353,"score_spread":0.2320667025494145,"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."}}