{"id":"W1607846922","doi":"10.1111/j.1467-9892.2012.00782.x","title":"Conditional variance estimation in regression models with long memory","year":2012,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Robert Bosch Stiftung","keywords":"Mathematics; Estimator; Heteroscedasticity; Kernel regression; Conditional variance; Econometrics; Parametric statistics; Kernel (algebra); Applied mathematics; Oracle; Statistics; Equivalence (formal languages); Autoregressive conditional heteroskedasticity; Computer science; Discrete mathematics","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":[0.0008521643,0.00009548326,0.0003972355,0.0002506185,0.00004322225,0.00002542766,0.0000892381,0.00004522725,0.0007364673],"category_scores_gemma":[0.0004887994,0.00006145611,0.0001008118,0.000527796,0.00004933909,0.0006628527,0.00001662563,0.000146192,0.00000586449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000492369,"about_ca_system_score_gemma":0.00004249298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008031976,"about_ca_topic_score_gemma":0.000005239001,"domain_scores_codex":[0.9988748,0.0001360893,0.0004388718,0.00006850713,0.000333248,0.0001485051],"domain_scores_gemma":[0.9986898,0.0004410633,0.0004640029,0.0001181636,0.0001956831,0.00009129621],"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":[0.002380369,0.002164512,0.05454039,0.0004946808,0.007630098,0.0003303949,0.005251683,0.3278761,0.001325604,0.549832,0.006084567,0.04208959],"study_design_scores_gemma":[0.000791741,0.0003030101,0.03825496,0.0003255781,0.002539551,0.0001888705,0.0003117342,0.3279133,0.0008878597,0.6281163,0.00003695927,0.0003301218],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04373092,0.00009894265,0.9549394,0.0001835389,0.00002615939,0.00003613917,0.000008923993,0.000005197448,0.0009708421],"genre_scores_gemma":[0.430847,0.00001448343,0.5686927,0.00002081308,0.00004729775,0.000001480629,0.000004034238,0.000006320119,0.0003658118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3871161,"threshold_uncertainty_score":0.8063801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04017970801909399,"score_gpt":0.3463336753297037,"score_spread":0.3061539673106097,"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."}}