{"id":"https://openalex.org/W7223650698","doi":"https://doi.org/10.65405/45x30y52","title":"Performance Comparison of SIMEX and Regression Calibration for Measurement Error Correction in Nonparametric Regression","display_name":"Performance Comparison of SIMEX and Regression Calibration for Measurement Error Correction in Nonparametric Regression","publication_year":2026,"publication_date":"2026-10-26","ids":{"openalex":"https://openalex.org/W7223650698","doi":"https://doi.org/10.65405/45x30y52"},"language":"en","primary_location":{"id":"doi:10.65405/45x30y52","doi":"https://doi.org/10.65405/45x30y52","is_oa":true,"landing_page_url":"https://doi.org/10.65405/45x30y52","pdf_url":"https://cjos.histr.edu.ly/index.php/journal/article/download/2615/2295","source":{"id":"https://openalex.org/S5407054142","display_name":"Comprehensive Journal of Science","issn_l":"2518-5799","issn":["2518-5799","3104-6266"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Comprehensive Journal of Science","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://cjos.histr.edu.ly/index.php/journal/article/download/2615/2295","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5152534283","display_name":"Nawal Mohamed Othman Mustafa","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I31439502","display_name":"University of Benghazi","ror":"https://ror.org/03fh7t044","country_code":"LY","type":"education","lineage":["https://openalex.org/I31439502"]}],"countries":["LY"],"is_corresponding":true,"raw_author_name":"Nawal Mohamed Othman Mustafa","raw_affiliation_strings":["Department of Statistics, Faculty of Science, Al-Marj,  University of Benghazi"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics, Faculty of Science, Al-Marj,  University of Benghazi","institution_ids":["https://openalex.org/I31439502"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5152534283"],"corresponding_institution_ids":["https://openalex.org/I31439502"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.81389412,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11","issue":"42","first_page":"1834","last_page":"1842"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14374","display_name":"Statistical Methods in Epidemiology","score":0.5913000106811523,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T14374","display_name":"Statistical Methods in Epidemiology","score":0.5913000106811523,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13141","display_name":"Statistical Methods and Applications","score":0.05350000038743019,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.04749999940395355,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/measurement-error-correction","display_name":"measurement error correction","score":0.9049999713897705},{"id":"https://openalex.org/keywords/simex","display_name":"SIMEX","score":0.9010000228881836},{"id":"https://openalex.org/keywords/regression-calibration","display_name":"regression calibration","score":0.9909999966621399},{"id":"https://openalex.org/keywords/nonparametric-regression","display_name":"nonparametric regression","score":0.9290000200271606},{"id":"https://openalex.org/keywords/nadaraya-watson-estimator","display_name":"Nadaraya-Watson estimator","score":0.9160000085830688},{"id":"https://openalex.org/keywords/kernel-smoothing","display_name":"kernel smoothing","score":0.4790000021457672},{"id":"https://openalex.org/keywords/attenuation-bias","display_name":"attenuation bias","score":0.8140000104904175},{"id":"https://openalex.org/keywords/over-smoothing","display_name":"over-smoothing","score":0.8209999799728394},{"id":"https://openalex.org/keywords/quadratic-extrapolation","display_name":"quadratic extrapolation","score":0.8830000162124634},{"id":"https://openalex.org/keywords/measurement-error-variance","display_name":"measurement error variance","score":0.49799999594688416}],"concepts":[{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.7242000102996826},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6772000193595886},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.6675999760627747},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6381000280380249},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.6086000204086304},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5123000144958496},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.421099990606308},{"id":"https://openalex.org/C179024874","wikidata":"https://www.wikidata.org/wiki/Q5395728","display_name":"Errors-in-variables models","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.4138999879360199},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C132459708","wikidata":"https://www.wikidata.org/wiki/Q744069","display_name":"Extrapolation","level":2,"score":0.382099986076355},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C40696583","wikidata":"https://www.wikidata.org/wiki/Q989120","display_name":"Type I and type II errors","level":2,"score":0.34139999747276306},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.3391999900341034},{"id":"https://openalex.org/C152732102","wikidata":"https://www.wikidata.org/wiki/Q13434396","display_name":"Variance inflation factor","level":4,"score":0.3276999890804291},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C74127309","wikidata":"https://www.wikidata.org/wiki/Q3455886","display_name":"Nonparametric regression","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C120068334","wikidata":"https://www.wikidata.org/wiki/Q45343","display_name":"Polynomial regression","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.2858000099658966},{"id":"https://openalex.org/C185265303","wikidata":"https://www.wikidata.org/wiki/Q7309537","display_name":"Regression dilution","level":4,"score":0.2849999964237213},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C164172150","wikidata":"https://www.wikidata.org/wiki/Q1782585","display_name":"Consistent estimator","level":4,"score":0.26080000400543213},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C19539793","wikidata":"https://www.wikidata.org/wiki/Q7449609","display_name":"Semiparametric regression","level":3,"score":0.257999986410141},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C35594927","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficient estimator","level":4,"score":0.2542000114917755},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.65405/45x30y52","doi":"https://doi.org/10.65405/45x30y52","is_oa":true,"landing_page_url":"https://doi.org/10.65405/45x30y52","pdf_url":"https://cjos.histr.edu.ly/index.php/journal/article/download/2615/2295","source":{"id":"https://openalex.org/S5407054142","display_name":"Comprehensive Journal of Science","issn_l":"2518-5799","issn":["2518-5799","3104-6266"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Comprehensive Journal of Science","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.65405/45x30y52","doi":"https://doi.org/10.65405/45x30y52","is_oa":true,"landing_page_url":"https://doi.org/10.65405/45x30y52","pdf_url":"https://cjos.histr.edu.ly/index.php/journal/article/download/2615/2295","source":{"id":"https://openalex.org/S5407054142","display_name":"Comprehensive Journal of Science","issn_l":"2518-5799","issn":["2518-5799","3104-6266"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Comprehensive Journal of Science","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223650698.pdf","grobid_xml":"https://content.openalex.org/works/W7223650698.grobid-xml"},"referenced_works_count":4,"referenced_works":["https://openalex.org/W2061250208","https://openalex.org/W2105228693","https://openalex.org/W3014896206","https://openalex.org/W4281392046"],"related_works":["https://openalex.org/W1972117954","https://openalex.org/W7208740891","https://openalex.org/W1964633433","https://openalex.org/W2414051388","https://openalex.org/W2037409196","https://openalex.org/W2165967940","https://openalex.org/W2276723768","https://openalex.org/W3118189890","https://openalex.org/W1559321620","https://openalex.org/W7134837480"],"abstract_inverted_index":{"It":[0],"is":[1],"well":[2],"established":[3],"that":[4,221],"measurement":[5,27,232],"errors":[6],"in":[7,12,18,126,163,209],"explanatory":[8],"variables":[9],"can":[10],"result":[11],"severe":[13],"attenuation":[14],"bias":[15,113],"and":[16,36,67,96,114,146,177,182,205,235],"over-smoothing":[17],"nonparametric":[19],"kernel":[20],"estimation.":[21],"This":[22],"study":[23,55],"compares":[24],"two":[25],"prominent":[26],"error":[28,69,118,186,225,233],"correction":[29,226],"procedures":[30],"Simulation-Extrapolation":[31],"(SIMEX)":[32],"using":[33,87],"quadratic":[34,211],"extrapolation":[35,212],"Regression":[37,171],"Calibration":[38,172],"(RC)":[39,173],"against":[40],"the":[41,130,202,223,231,236],"standard,":[42],"uncorrected":[43,131],"Nadaraya\u2013Watson":[44],"(NW)":[45],"estimator":[46],"under":[47,116,167,214],"classical":[48],"additive":[49],"error.":[50],"A":[51],"Monte":[52],"Carlo":[53],"simulation":[54],"was":[56,85],"conducted":[57],"across":[58,197],"three":[59,68],"sample":[60,165,198],"sizes":[61,166],"(n":[62],"=":[63,73,121,138,144,153],"100,":[64,139],"200,":[65,145],"500)":[66],"variance":[70,119,203],"levels":[71,187],"(":[72,120,188],"0.1,":[74],"0.3,":[75],"0.5)":[76],"applied":[77],"to":[78,129,140,149,161,195],"a":[79,101],"non-linear":[80],"target":[81],"curve.":[82],"Estimator":[83],"performance":[84,104,206,238],"evaluated":[86],"Mean":[88,92],"Squared":[89],"Error":[90,94],"(MSE),":[91],"Absolute":[93],"(MAE)":[95],"Bias.":[97],"The":[98],"findings":[99],"reveal":[100],"clear,":[102],"complementary":[103],"pattern":[105],"supported":[106],"by":[107,159],"empirical":[108,218],"evidence:":[109],"SIMEX":[110,156],"optimally":[111],"reduces":[112,157],"MAE":[115,158,183],"low":[117,168],"0.1),":[122],"achieving":[123],"significant":[124],"reductions":[125,192],"MSE":[127,181,191],"compared":[128],"estimator,":[132],"ranging":[133],"from":[134],"18.1%":[135],"at":[136,142,151,184],"n":[137,143,152],"24.0%":[141],"reaching":[147],"up":[148,160],"36.1%":[150],"500.":[154],"Furthermore,":[155],"21.0%":[162],"large":[164],"noise.":[169,216],"Conversely,":[170],"exhibits":[174],"superior":[175],"stability":[176],"consistently":[178],"lower":[179],"overall":[180],"moderate-to-severe":[185],"0.3),":[189],"offering":[190],"of":[193],"10.3%":[194],"14.4%":[196],"sizes,":[199],"while":[200],"avoiding":[201],"inflation":[204],"degradation":[207],"inherent":[208],"SIMEX's":[210],"step":[213],"high":[215],"These":[217],"results":[219],"demonstrate":[220],"selecting":[222],"optimal":[224],"method":[227],"depends":[228],"directly":[229],"on":[230],"magnitude":[234],"targeted":[237],"metric.":[239]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-27T00:00:00"}