{"id":"W2022461842","doi":"10.1016/s0167-7152(02)00353-x","title":"Testing variances in wavelet regression models","year":2002,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homoscedasticity; Heteroscedasticity; Mathematics; Statistics; Nuisance parameter; Econometrics; Wavelet; Statistical hypothesis testing; Estimator; Artificial intelligence; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03984959,0.00193865,0.001798838,0.001881747,0.0009901135,0.00300373,0.002897594,0.003440323,0.002715802],"category_scores_gemma":[0.2704458,0.001592667,0.002234755,0.001550904,0.003741426,0.005589824,0.002851448,0.004250914,0.0006338959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007267728,"about_ca_system_score_gemma":0.001884685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001652682,"about_ca_topic_score_gemma":0.001095245,"domain_scores_codex":[0.9655484,0.02454848,0.001482877,0.004330677,0.002747669,0.001341831],"domain_scores_gemma":[0.5521857,0.4269433,0.006037027,0.009961037,0.003491728,0.001381305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00491697,0.001140929,0.1339223,0.0006122718,0.003516991,0.00169976,0.001676914,0.3709047,0.01166702,0.1863385,0.004140427,0.2794632],"study_design_scores_gemma":[0.0003204643,0.0006830925,0.01478379,0.00007612084,0.0003585924,0.0003031823,0.0004277867,0.7854528,0.004750595,0.1920235,0.0007434073,0.0000767243],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2747494,0.0002482228,0.7221549,0.0006807779,0.0001099841,0.00004383232,0.0001448975,0.0004859706,0.001382045],"genre_scores_gemma":[0.9411676,0.0002097491,0.05621804,0.0001623755,0.000149487,0.0001388061,0.0005729016,0.0003353649,0.001045718],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03984959,"threshold_uncertainty_score":0.2107473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07276282801526988,"score_gpt":0.2800563709657328,"score_spread":0.2072935429504629,"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."}}