{"id":"W2555972382","doi":"10.1016/j.jmva.2017.07.005","title":"Multivariate intensity estimation via hyperbolic wavelet selection","year":2017,"lang":"en","type":"article","venue":"Journal of Multivariate Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematics; Estimator; Smoothness; Wavelet; Curse of dimensionality; Bivariate analysis; Multivariate statistics; Biorthogonal system; Biorthogonal wavelet; Applied mathematics; Oracle; Function (biology); Selection (genetic algorithm); Mathematical optimization; Algorithm; Statistics; Wavelet transform; Mathematical analysis; Artificial intelligence; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001502176,0.0006199465,0.0008685663,0.0007017145,0.0002667806,0.0009248635,0.0009739437,0.000869008,0.001921981],"category_scores_gemma":[0.003521425,0.0007124491,0.0008700283,0.0007643612,0.0006268905,0.001380451,0.001786111,0.001268863,0.0007366784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000285929,"about_ca_system_score_gemma":0.0006231062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000639939,"about_ca_topic_score_gemma":0.0009650426,"domain_scores_codex":[0.9995496,0.0001768429,0.00002375116,0.00008149157,0.000135256,0.0000330762],"domain_scores_gemma":[0.9989184,0.000561053,0.0001118408,0.0001754273,0.0001792477,0.00005404838],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005071455,0.000165589,0.003072201,0.0002793148,0.0002832763,0.0002095941,0.0002322475,0.2110606,0.08464219,0.1101025,0.003710883,0.5857344],"study_design_scores_gemma":[0.00001911277,0.00002695205,0.0005465929,0.000007183488,0.00002428757,0.00005962831,0.00001200711,0.9792935,0.006349308,0.01259964,0.001048053,0.00001376754],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006658223,0.00004280165,0.9928325,0.00007146821,0.00001296048,0.000009166103,0.00001371877,0.00006943723,0.0002895821],"genre_scores_gemma":[0.2127652,0.0004034124,0.7807645,0.0001255549,0.0001046408,0.00009144318,0.000202885,0.0002030672,0.00533931],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001921981,"threshold_uncertainty_score":0.007944345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02796914760446475,"score_gpt":0.3236232788816691,"score_spread":0.2956541312772044,"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."}}