{"id":"W1997577129","doi":"10.1081/etc-100104079","title":"A CONSISTENT MODEL SPECIFICATION TEST FOR A REGRESSION FUNCTION BASED ON NONPARAMETRIC WAVELET ESTIMATION","year":2001,"lang":"en","type":"article","venue":"Econometric Reviews","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Guelph","funders":"","keywords":"Nonparametric regression; Nonparametric statistics; Test statistic; Mathematics; Statistics; Kernel (algebra); Parametric statistics; Monte Carlo method; Statistic; Semiparametric regression; Kernel density estimation; Econometrics; Statistical hypothesis testing; Applied mathematics","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":[0.002265358,0.0002039217,0.0004110758,0.001376276,0.0001690048,0.0002170626,0.0003992598,0.00007670386,0.0000322083],"category_scores_gemma":[0.002664646,0.000162754,0.0002193525,0.003209469,0.00001812255,0.0004749256,0.00003251641,0.0001109389,0.0002342645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001960671,"about_ca_system_score_gemma":0.00006460219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002278495,"about_ca_topic_score_gemma":2.796546e-7,"domain_scores_codex":[0.9981951,0.0001519683,0.0006630686,0.0005610101,0.0001800476,0.0002487781],"domain_scores_gemma":[0.9974309,0.001085929,0.0004622704,0.0007996894,0.0001210076,0.0001002374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003264544,0.00017614,0.00008771796,0.0000626774,0.000003898921,0.000001159157,0.00001855043,0.009279495,0.00008291014,0.001757086,0.005104927,0.9833928],"study_design_scores_gemma":[0.0006327878,0.0002875442,0.0009186096,0.00008251164,0.00001872806,0.000005596735,9.300867e-7,0.9417708,0.0001648392,0.002354397,0.0535792,0.000184078],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003159362,0.002158301,0.9908004,0.000555272,0.0002954693,0.001035592,0.000003484559,0.00007203039,0.004763479],"genre_scores_gemma":[0.0988465,0.001424221,0.8962038,0.001487569,0.0001290897,0.0003650815,0.00004143178,0.00002209065,0.001480212],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9832087,"threshold_uncertainty_score":0.6636915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1098881263387143,"score_gpt":0.3276402181372694,"score_spread":0.2177520917985551,"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."}}