{"id":"W1609600434","doi":"10.48550/arxiv.0902.2117","title":"Deconvolution density estimation with heteroscedastic errors using SIMEX","year":2009,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Heteroscedasticity; Deconvolution; Estimation; Econometrics; Computer science; Statistics; Mathematics; Economics","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":[0.004418206,0.0008225722,0.0009877566,0.000944971,0.0004590499,0.0007104506,0.001108942,0.001078892,0.001293441],"category_scores_gemma":[0.01031312,0.0005276607,0.0008433191,0.0007201192,0.001008809,0.001332422,0.001767812,0.0009623542,0.0003592722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006272464,"about_ca_system_score_gemma":0.001080479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002096189,"about_ca_topic_score_gemma":0.001596046,"domain_scores_codex":[0.9988999,0.0007062219,0.00005105492,0.0001226769,0.0001827564,0.00003732288],"domain_scores_gemma":[0.9957694,0.002967766,0.0003724612,0.0004732543,0.0003638414,0.00005330217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006487117,0.0001133508,0.005213962,0.0002454563,0.000219982,0.0003170618,0.0003295,0.7543932,0.01106552,0.08881029,0.0007605532,0.1378824],"study_design_scores_gemma":[0.0000214687,0.0000377212,0.000333169,0.0000165658,0.000008250016,0.00009902696,0.00001276871,0.9831775,0.004311101,0.01116067,0.0008055309,0.00001629015],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007286754,0.00005428242,0.9922691,0.00003301243,0.000006800284,0.00001460295,0.00001382507,0.0001478565,0.0001737868],"genre_scores_gemma":[0.2269191,0.0002226981,0.7708405,0.00009608068,0.00002368199,0.000162905,0.0001775334,0.00009032359,0.00146725],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004418206,"threshold_uncertainty_score":0.02336597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2151066393699005,"score_gpt":0.4048737284980667,"score_spread":0.1897670891281662,"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."}}