{"id":"https://openalex.org/W3212943769","doi":"https://doi.org/10.1080/03610918.2021.2001656","title":"Nonlinear multiplicative distortion regression models with second-order estimation","display_name":"Nonlinear multiplicative distortion regression models with second-order estimation","publication_year":2021,"publication_date":"2021-11-11","ids":{"openalex":"https://openalex.org/W3212943769","doi":"https://doi.org/10.1080/03610918.2021.2001656","mag":"3212943769"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2021.2001656","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.2001656","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5020692924","display_name":"Jun Zhang","orcid":"https://orcid.org/0000-0003-4332-5182"},"institutions":[{"id":"https://openalex.org/I180726961","display_name":"Shenzhen University","ror":"https://ror.org/01vy4gh70","country_code":"CN","type":"education","lineage":["https://openalex.org/I180726961"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jun Zhang","raw_affiliation_strings":["College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China"],"affiliations":[{"raw_affiliation_string":"College of Mathematics and Statistics, Institute of Statistical Sciences, Shenzhen University, Shenzhen, China","institution_ids":["https://openalex.org/I180726961"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5020692924"],"corresponding_institution_ids":["https://openalex.org/I180726961"],"apc_list":null,"apc_paid":null,"fwci":1.392,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.83671747,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"52","issue":"12","first_page":"5894","last_page":"5924"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9988999962806702,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9988999962806702,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9620000123977661,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.7708386182785034},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.5985876321792603},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5800226330757141},{"id":"https://openalex.org/keywords/covariate","display_name":"Covariate","score":0.5567271709442139},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.5314834117889404},{"id":"https://openalex.org/keywords/nonlinear-regression","display_name":"Nonlinear regression","score":0.5059816241264343},{"id":"https://openalex.org/keywords/ordinary-least-squares","display_name":"Ordinary least squares","score":0.44548267126083374},{"id":"https://openalex.org/keywords/conditional-expectation","display_name":"Conditional expectation","score":0.43748176097869873},{"id":"https://openalex.org/keywords/non-linear-least-squares","display_name":"Non-linear least squares","score":0.42462679743766785},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.42335233092308044},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3544068932533264},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.3022909164428711}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.7708386182785034},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.5985876321792603},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5800226330757141},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.5567271709442139},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.5314834117889404},{"id":"https://openalex.org/C46889948","wikidata":"https://www.wikidata.org/wiki/Q2755024","display_name":"Nonlinear regression","level":3,"score":0.5059816241264343},{"id":"https://openalex.org/C99656134","wikidata":"https://www.wikidata.org/wiki/Q2912993","display_name":"Ordinary least squares","level":2,"score":0.44548267126083374},{"id":"https://openalex.org/C186215838","wikidata":"https://www.wikidata.org/wiki/Q772232","display_name":"Conditional expectation","level":2,"score":0.43748176097869873},{"id":"https://openalex.org/C45923927","wikidata":"https://www.wikidata.org/wiki/Q3319230","display_name":"Non-linear least squares","level":3,"score":0.42462679743766785},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.42335233092308044},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3544068932533264},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3022909164428711}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2021.2001656","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2021.2001656","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.4000000059604645}],"awards":[{"id":"https://openalex.org/G6558122914","display_name":null,"funder_award_id":"2020A1515010372","funder_id":"https://openalex.org/F4320321921","funder_display_name":"Natural Science Foundation of Guangdong Province"}],"funders":[{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1971713783","https://openalex.org/W1974807620","https://openalex.org/W1981095295","https://openalex.org/W2020371156","https://openalex.org/W2021075269","https://openalex.org/W2034180713","https://openalex.org/W2064718511","https://openalex.org/W2070494064","https://openalex.org/W2101426969","https://openalex.org/W2104842913","https://openalex.org/W2550910743","https://openalex.org/W2560114309","https://openalex.org/W2766509234","https://openalex.org/W2792413178","https://openalex.org/W2920899562","https://openalex.org/W2921519938","https://openalex.org/W2954732189","https://openalex.org/W2954888364","https://openalex.org/W2962856767","https://openalex.org/W2998660899","https://openalex.org/W3015112821","https://openalex.org/W3015211288","https://openalex.org/W3023193272","https://openalex.org/W3044737039","https://openalex.org/W3102693992","https://openalex.org/W3111118549","https://openalex.org/W3156146743","https://openalex.org/W3192281216"],"related_works":["https://openalex.org/W2026673180","https://openalex.org/W1959514624","https://openalex.org/W200101083","https://openalex.org/W2939592218","https://openalex.org/W2359835307","https://openalex.org/W1984191672","https://openalex.org/W2167126078","https://openalex.org/W4235679112","https://openalex.org/W4250719734","https://openalex.org/W4312475898"],"abstract_inverted_index":{"This":[0],"paper":[1],"considers":[2],"nonlinear":[3,72,133,147],"regression":[4,78],"models":[5],"when":[6],"neither":[7],"the":[8,12,28,37,40,44,52,62,69,77,91,106,112,117,122,145],"response":[9,45,54,108],"variable":[10,46,55],"nor":[11],"covariates":[13],"can":[14],"be":[15],"directly":[16],"observed,":[17],"but":[18],"are":[19,80,141],"measured":[20],"with":[21,137],"multiplicative":[22],"distortion":[23],"measurement":[24],"errors.":[25],"We":[26,95],"proposed":[27],"second":[29,58,70,131],"order":[30,71,132],"least":[31,73,134,148],"squares":[32,74,135,149],"estimator":[33],"by":[34,89],"including":[35],"in":[36],"criterion":[38],"function":[39],"distance":[41],"of":[42,68],"both":[43],"to":[47,56,98,104],"its":[48,57],"conditional":[49,59,113,118],"mean":[50,115],"and":[51,109,121],"squared":[53],"moment":[60],"given":[61],"predictor":[63],"variables.":[64],"The":[65,125],"asymptotic":[66,92],"results":[67],"estimators":[75,136],"for":[76],"parameter":[79],"derived.":[81],"As":[82],"a":[83],"by-product,":[84],"we":[85],"construct":[86],"confidence":[87],"intervals":[88],"estimating":[90],"covariance":[93],"matrices.":[94],"also":[96],"consider":[97],"use":[99],"three":[100,138],"existing":[101],"calibration":[102,120,139],"procedures":[103,140],"calibrate":[105],"unknown":[107],"covariates,":[110],"namely,":[111],"absolute":[114],"calibration,":[116],"variance":[119],"logarithmic":[123],"calibration.":[124],"simulation":[126],"studies":[127],"shows":[128],"that":[129],"these":[130],"all":[142],"superior":[143],"than":[144],"ordinary":[146],"estimators.":[150]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}