{"id":"https://openalex.org/W4388745947","doi":"https://doi.org/10.5705/ss.202022.0331","title":"A Linear Errors-in-Variables Model with Unknown Heteroscedastic Measurement Errors","display_name":"A Linear Errors-in-Variables Model with Unknown Heteroscedastic Measurement Errors","publication_year":2023,"publication_date":"2023-11-16","ids":{"openalex":"https://openalex.org/W4388745947","doi":"https://doi.org/10.5705/ss.202022.0331"},"language":"en","primary_location":{"id":"doi:10.5705/ss.202022.0331","doi":"https://doi.org/10.5705/ss.202022.0331","is_oa":true,"landing_page_url":"https://doi.org/10.5705/ss.202022.0331","pdf_url":"https://doi.org/10.5705/ss.202022.0331","source":{"id":"https://openalex.org/S23408671","display_name":"Statistica Sinica","issn_l":"1017-0405","issn":["1017-0405","1996-8507"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-2","norway-1","poland-140","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4362561675","host_organization_name":"Institute of Statistical Science","host_organization_lineage":["https://openalex.org/P4362561675"],"host_organization_lineage_names":["Institute of Statistical Science"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistica Sinica","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.5705/ss.202022.0331","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042318638","display_name":"Linh H. Nghiem","orcid":"https://orcid.org/0000-0003-2874-9067","observed_orcids":["https://orcid.org/0000-0003-2874-9067"]},"institutions":[{"id":"https://openalex.org/I128956969","display_name":"Texas Christian University","ror":"https://ror.org/054b0b564","country_code":"US","type":"education","lineage":["https://openalex.org/I128956969"]},{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]},{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["AU","US","ZA"],"is_corresponding":true,"raw_author_name":"Linh H. Nghiem","raw_affiliation_strings":["Texas Christian University &","University of Sydney","Texas Christian University & University of Johannesburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas Christian University &","institution_ids":["https://openalex.org/I128956969"]},{"raw_affiliation_string":"University of Sydney","institution_ids":["https://openalex.org/I129604602"]},{"raw_affiliation_string":"Texas Christian University & University of Johannesburg","institution_ids":["https://openalex.org/I128956969","https://openalex.org/I24027795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069269662","display_name":"Cornelis J. Potgieter","orcid":"https://orcid.org/0000-0002-1995-6817","observed_orcids":["https://orcid.org/0000-0002-1995-6817"]},"institutions":[{"id":"https://openalex.org/I24027795","display_name":"University of Johannesburg","ror":"https://ror.org/04z6c2n17","country_code":"ZA","type":"education","lineage":["https://openalex.org/I24027795"]}],"countries":["ZA"],"is_corresponding":false,"raw_author_name":"Cornelis J. Potgieter","raw_affiliation_strings":["University of Johannesburg"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Johannesburg","institution_ids":["https://openalex.org/I24027795"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5042318638"],"corresponding_institution_ids":["https://openalex.org/I128956969","https://openalex.org/I129604602","https://openalex.org/I24027795"],"apc_list":null,"apc_paid":null,"fwci":0.4509,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6105005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13487","display_name":"Statistical and numerical algorithms","score":0.4377000033855438,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T13487","display_name":"Statistical and numerical algorithms","score":0.4377000033855438,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.1914999932050705,"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/T10136","display_name":"Statistical Methods and Inference","score":0.07429999858140945,"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","display_name":"measurement error","score":0.8389999866485596},{"id":"https://openalex.org/keywords/heteroscedastic-measurement-error","display_name":"heteroscedastic measurement error","score":0.9710000157356262},{"id":"https://openalex.org/keywords/unknown-error-distribution","display_name":"unknown error distribution","score":0.7059999704360962},{"id":"https://openalex.org/keywords/generalized-method-of-moments","display_name":"generalized method of moments","score":0.8690000176429749},{"id":"https://openalex.org/keywords/scattering-phase-function","display_name":"phase function","score":0.75},{"id":"https://openalex.org/keywords/parameter-estimation","display_name":"parameter estimation","score":0.718999981880188},{"id":"https://openalex.org/keywords/consistency","display_name":"consistency","score":0.8040000200271606},{"id":"https://openalex.org/keywords/asymptotic-normality","display_name":"asymptotic normality","score":0.9990000128746033},{"id":"https://openalex.org/keywords/asymptotic-covariance","display_name":"asymptotic covariance","score":0.8240000009536743},{"id":"https://openalex.org/keywords/bootstrap-standard-errors","display_name":"bootstrap standard errors","score":0.7440000176429749},{"id":"https://openalex.org/keywords/non-gaussian-measurement-errors","display_name":"non-Gaussian measurement errors","score":0.8510000109672546}],"concepts":[{"id":"https://openalex.org/C101104100","wikidata":"https://www.wikidata.org/wiki/Q1063540","display_name":"Heteroscedasticity","level":2,"score":0.8096029758453369},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.577613353729248},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5309785008430481},{"id":"https://openalex.org/C19619285","wikidata":"https://www.wikidata.org/wiki/Q196372","display_name":"Observational error","level":2,"score":0.5108403563499451},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4585322141647339},{"id":"https://openalex.org/C55769033","wikidata":"https://www.wikidata.org/wiki/Q7049018","display_name":"Non-sampling error","level":2,"score":0.43399906158447266},{"id":"https://openalex.org/C179024874","wikidata":"https://www.wikidata.org/wiki/Q5395728","display_name":"Errors-in-variables models","level":2,"score":0.42821168899536133},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4012306332588196}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5705/ss.202022.0331","doi":"https://doi.org/10.5705/ss.202022.0331","is_oa":true,"landing_page_url":"https://doi.org/10.5705/ss.202022.0331","pdf_url":"https://doi.org/10.5705/ss.202022.0331","source":{"id":"https://openalex.org/S23408671","display_name":"Statistica Sinica","issn_l":"1017-0405","issn":["1017-0405","1996-8507"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-2","norway-1","poland-140","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4362561675","host_organization_name":"Institute of Statistical Science","host_organization_lineage":["https://openalex.org/P4362561675"],"host_organization_lineage_names":["Institute of Statistical Science"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistica Sinica","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2310.13874","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2310.13874","pdf_url":"https://arxiv.org/pdf/2310.13874","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2310.13874","doi":"https://doi.org/10.48550/arxiv.2310.13874","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2310.13874","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5705/ss.202022.0331","doi":"https://doi.org/10.5705/ss.202022.0331","is_oa":true,"landing_page_url":"https://doi.org/10.5705/ss.202022.0331","pdf_url":"https://doi.org/10.5705/ss.202022.0331","source":{"id":"https://openalex.org/S23408671","display_name":"Statistica Sinica","issn_l":"1017-0405","issn":["1017-0405","1996-8507"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-a","anvur-class-a","anvur-scientific","cwts-core","dongbi-b","jufo-2","norway-1","poland-140","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4362561675","host_organization_name":"Institute of Statistical Science","host_organization_lineage":["https://openalex.org/P4362561675"],"host_organization_lineage_names":["Institute of Statistical Science"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Statistica Sinica","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4388745947.pdf","grobid_xml":"https://content.openalex.org/works/W4388745947.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W611112179","https://openalex.org/W1511751337","https://openalex.org/W1512882103","https://openalex.org/W1783514032","https://openalex.org/W1911864595","https://openalex.org/W2003531980","https://openalex.org/W2010725596","https://openalex.org/W2011287710","https://openalex.org/W2028995298","https://openalex.org/W2033612661","https://openalex.org/W2058604421","https://openalex.org/W2080040321","https://openalex.org/W2089583970","https://openalex.org/W2104343088","https://openalex.org/W2161676148","https://openalex.org/W2164406114","https://openalex.org/W2314496443","https://openalex.org/W2884494317","https://openalex.org/W2902428698","https://openalex.org/W3123423818","https://openalex.org/W3202106223","https://openalex.org/W4243563432","https://openalex.org/W4254178252"],"related_works":["https://openalex.org/W2104550218","https://openalex.org/W2902428698","https://openalex.org/W4220970141","https://openalex.org/W2726798140","https://openalex.org/W1965272286","https://openalex.org/W811255732","https://openalex.org/W2074153536","https://openalex.org/W3201970934","https://openalex.org/W2329188561","https://openalex.org/W7115178070"],"abstract_inverted_index":{"In":[0],"the":[1,24,62,87,92,118],"classic":[2],"measurement":[3,26,119],"error":[4,27,95],"framework,":[5],"covariates":[6,66],"are":[7],"contaminated":[8],"by":[9],"independent":[10],"additive":[11],"noise.This":[12],"paper":[13],"considers":[14],"parameter":[15],"estimation":[16],"in":[17,113],"such":[18],"a":[19,34,44,49,99],"linear":[20],"errors-in-variables":[21],"model":[22],"where":[23],"unknown":[25],"distribution":[28],"is":[29,72,89,96,105],"heteroscedastic":[30],"across":[31],"observations.We":[32],"propose":[33],"new":[35,70],"generalized":[36],"method":[37],"of":[38,86],"moment":[39,45],"(GMM)":[40],"estimator":[41,71,88,104],"that":[42],"combines":[43],"correction":[46],"approach":[47],"and":[48,77,91],"phase":[50],"functionbased":[51],"approach.The":[52],"former":[53],"requires":[54],"distributions":[55],"to":[56,74,107],"have":[57,108],"four":[58],"finite":[59,110],"moments,":[60],"while":[61],"latter":[63],"relies":[64],"on":[65],"having":[67],"asymmetric":[68],"distributions.The":[69],"shown":[73],"be":[75],"consistent":[76],"asymptotically":[78],"normal":[79],"under":[80],"appropriate":[81],"regularity":[82],"conditions.The":[83],"asymptotic":[84],"covariance":[85],"derived,":[90],"estimated":[93],"standard":[94],"computed":[97],"using":[98],"fast":[100],"bootstrap":[101],"procedure.The":[102],"GMM":[103],"demonstrated":[106],"strong":[109],"sample":[111],"performance":[112],"numerical":[114],"studies,":[115],"especially":[116],"when":[117],"errors":[120],"follow":[121],"non-Gaussian":[122],"distributions.":[123]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2023-11-17T00:00:00"}