{"id":"W2124975242","doi":"10.1080/10629360600831711","title":"Finite sample penalization in adaptive density deconvolution","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mathematics; Deconvolution; Estimator; Independent and identically distributed random variables; Kernel density estimation; Robustness (evolution); Adaptive estimator; Density estimation; Statistics; Dependency (UML); Applied mathematics; Kernel (algebra); Algorithm; Random variable; Combinatorics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004850592,0.0005300056,0.0007843549,0.0005475249,0.0003172961,0.0006283236,0.001367109,0.001531457,0.0008669127],"category_scores_gemma":[0.02806639,0.0004480874,0.0005683613,0.0005346439,0.001961426,0.001349291,0.001430759,0.001154254,0.0001635554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006721372,"about_ca_system_score_gemma":0.0008369516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003050711,"about_ca_topic_score_gemma":0.002027082,"domain_scores_codex":[0.9987175,0.0008436046,0.00003665893,0.0001243718,0.0002260115,0.00005185054],"domain_scores_gemma":[0.9886881,0.009789468,0.0004704995,0.0004987129,0.000445233,0.0001079609],"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":[0.0002322134,0.00004084404,0.00159465,0.0001253431,0.00006351488,0.0001520686,0.0001316763,0.854502,0.003082518,0.10175,0.0006014365,0.03772368],"study_design_scores_gemma":[0.00001277595,0.00001632708,0.0001236182,0.000005464716,0.00000402619,0.00002145436,0.000004989213,0.9850032,0.0006818903,0.01386786,0.0002516622,0.000006783401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01465263,0.000134574,0.9846124,0.0001345079,0.00001297748,0.00001532663,0.00001102988,0.00009424967,0.0003322863],"genre_scores_gemma":[0.4291238,0.0002796983,0.5681523,0.000154189,0.00005362909,0.0001913882,0.00007799803,0.0001148102,0.001852113],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004850592,"threshold_uncertainty_score":0.02565271,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1102432596404326,"score_gpt":0.4171216185458697,"score_spread":0.3068783589054371,"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."}}