{"id":"W2964181036","doi":"10.1111/jtsa.12229","title":"A plug-in bandwidth selection procedure for long run covariance estimation with stationary functional time series","year":2016,"lang":"en","type":"article","venue":"ANU Open Research (Australian National University)","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bandwidth (computing); Estimator; Mathematics; Covariance; Applied mathematics; Covariance function; Covariance matrix; Mathematical optimization; Algorithm; Statistics; Computer science; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.001106616,0.0001044517,0.0001724294,0.0005251287,0.0003371224,0.0001087855,0.0002641414,0.00009524303,0.0003453001],"category_scores_gemma":[0.0003083927,0.0001043967,0.00003568828,0.0008934333,0.0001028124,0.001972979,0.00006047239,0.000144505,0.0001301978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005923294,"about_ca_system_score_gemma":0.0004555223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003195602,"about_ca_topic_score_gemma":0.0004226106,"domain_scores_codex":[0.9988064,0.0000408045,0.0002352998,0.0004400316,0.0001737008,0.0003038012],"domain_scores_gemma":[0.9990021,0.0001932174,0.0001135156,0.00009020789,0.0005303286,0.00007062145],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.002303711,0.0002382999,0.1072976,0.00007493889,0.00006533065,0.00001232929,0.0001549628,0.007577125,0.000117148,0.8696689,0.01084005,0.001649625],"study_design_scores_gemma":[0.007411948,0.0009039462,0.5405907,0.0003487008,0.00001091253,0.0000209319,0.0001763442,0.0551122,0.0006167484,0.2959613,0.09804636,0.0007998377],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5170996,0.00007627656,0.4041737,0.03673305,0.0002358697,0.005963861,0.002462277,0.0001371001,0.03311829],"genre_scores_gemma":[0.8853471,0.0000317288,0.01321097,0.00003630267,0.00007788314,0.00003950654,0.0001928955,0.00001875945,0.1010448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5737076,"threshold_uncertainty_score":0.4257172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1248838639356019,"score_gpt":0.3064421335262806,"score_spread":0.1815582695906788,"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."}}