Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary w...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Patriota, Alexandre G, Lemonte, Artur J, Bolfarine, Heleno
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.08.2015
Subjects
Online AccessGet full text
ISSN2331-8422
DOI10.48550/arxiv.0903.3146

Cover

Abstract This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.
AbstractList This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.
This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set.
Author Lemonte, Artur J
Bolfarine, Heleno
Patriota, Alexandre G
Author_xml – sequence: 1
  givenname: Alexandre
  surname: Patriota
  middlename: G
  fullname: Patriota, Alexandre G
– sequence: 2
  givenname: Artur
  surname: Lemonte
  middlename: J
  fullname: Lemonte, Artur J
– sequence: 3
  givenname: Heleno
  surname: Bolfarine
  fullname: Bolfarine, Heleno
BackLink https://doi.org/10.1007/s00362-009-0243-7$$DView published paper (Access to full text may be restricted)
https://doi.org/10.48550/arXiv.0903.3146$$DView paper in arXiv
BookMark eNotz71PwzAQBXALgUQp3ZmQJeYU22cn9ogqPipVYilz5CQX1W0SF7uNyn-PS5lueE-n97sj14MfkJAHzuZSK8WebTi5cc4MgzlwmV-RiQDgmZZC3JJZjFvGmMgLoRRMyNey3wc_YkN7e3L9saed22HnNt43FOPB9fbgQ6RuoJZu8IDBxx02NiU1xRBSlrkhG21wtuow0t432N2Tm9Z2EWf_d0rWb6_rxUe2-nxfLl5WmVUcMo4KLMqqFdaoHCtjUGjeAhO6tqaGXLQNEw1TFiSTldGFLgqNRraM15YzmJLHy9s_crkPaW34Kc_08kxPhadLIRm_j4lTbv0xDGlSKZiGXBdSAfwC5B1edQ
ContentType Paper
Journal Article
Copyright 2015. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
http://arxiv.org/licenses/nonexclusive-distrib/1.0
Copyright_xml – notice: 2015. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: http://arxiv.org/licenses/nonexclusive-distrib/1.0
DBID 8FE
8FG
ABJCF
ABUWG
AFKRA
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
HCIFZ
L6V
M7S
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
EPD
GOX
DOI 10.48550/arxiv.0903.3146
DatabaseName ProQuest SciTech Collection
ProQuest Technology Collection
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central Essentials - QC
ProQuest Central
Technology Collection
ProQuest One Community College
ProQuest Central
SciTech Premium Collection (via ProQuest)
ProQuest Engineering Collection
Engineering Database
ProQuest Central Premium
ProQuest One Academic
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
Engineering collection
arXiv Statistics
arXiv.org
DatabaseTitle Publicly Available Content Database
Engineering Database
Technology Collection
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Central (Alumni Edition)
SciTech Premium Collection
ProQuest One Community College
ProQuest Technology Collection
ProQuest SciTech Collection
ProQuest Central China
ProQuest Central
ProQuest One Applied & Life Sciences
ProQuest Engineering Collection
ProQuest One Academic UKI Edition
ProQuest Central Korea
Materials Science & Engineering Collection
ProQuest Central (New)
ProQuest One Academic
ProQuest One Academic (New)
Engineering Collection
DatabaseTitleList
Publicly Available Content Database
Database_xml – sequence: 1
  dbid: GOX
  name: arXiv.org
  url: http://arxiv.org/find
  sourceTypes: Open Access Repository
– sequence: 2
  dbid: 8FG
  name: ProQuest Technology Collection
  url: https://search.proquest.com/technologycollection1
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Physics
EISSN 2331-8422
ExternalDocumentID 0903_3146
Genre Working Paper/Pre-Print
GroupedDBID 8FE
8FG
ABJCF
ABUWG
AFKRA
ALMA_UNASSIGNED_HOLDINGS
AZQEC
BENPR
BGLVJ
CCPQU
DWQXO
FRJ
HCIFZ
L6V
M7S
M~E
PHGZM
PHGZT
PIMPY
PKEHL
PQEST
PQGLB
PQQKQ
PQUKI
PRINS
PTHSS
EPD
GOX
ID FETCH-LOGICAL-a513-1e53ae4bf2a956eb99e281f3028ca9c362fd02d05a3404b9878778e94f01ca103
IEDL.DBID 8FG
IngestDate Tue Jul 22 23:10:21 EDT 2025
Mon Jun 30 09:31:41 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-a513-1e53ae4bf2a956eb99e281f3028ca9c362fd02d05a3404b9878778e94f01ca103
Notes SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
OpenAccessLink https://www.proquest.com/docview/2083687453?pq-origsite=%requestingapplication%
PQID 2083687453
PQPubID 2050157
ParticipantIDs arxiv_primary_0903_3146
proquest_journals_2083687453
PublicationCentury 2000
PublicationDate 20150826
PublicationDateYYYYMMDD 2015-08-26
PublicationDate_xml – month: 08
  year: 2015
  text: 20150826
  day: 26
PublicationDecade 2010
PublicationPlace Ithaca
PublicationPlace_xml – name: Ithaca
PublicationTitle arXiv.org
PublicationYear 2015
Publisher Cornell University Library, arXiv.org
Publisher_xml – name: Cornell University Library, arXiv.org
SSID ssj0002672553
Score 1.5733184
SecondaryResourceType preprint
Snippet This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in...
This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in...
SourceID arxiv
proquest
SourceType Open Access Repository
Aggregation Database
SubjectTerms Analytical chemistry
Astrophysics
Bias
Computer simulation
Economic models
Epidemiology
Mathematical models
Maximum likelihood estimators
Monte Carlo simulation
Organic chemistry
Statistics - Methodology
SummonAdditionalLinks – databaseName: arXiv.org
  dbid: GOX
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwdV09T8MwELXaTiwIxFdpAQ-sFklsp_aIEFWFBCyt1C2ynYuI2qQoaav-fHxJyoJY_bHc2X73zr5nQh5daK00EDGuVcCEdgFTGeBbV-uk8Xglmzzk-0c8W4i3pVz2yMOxFsZUh3zf6gPb-gmTCJ5VirhP-j5OwFrez2V72dgocXXDf4f5CLNp-XOwNmgxPSOnXZhHn1u_nJMelBdk0TJ4SGlhDnmxK-g6X8E6R2VhimoXBTLgmuYlNfQL36ls6hWkBqWUKVSV72N5yfae3mLBU02bb2wuyXz6On-Zse5bA2ZkyFkIkhsQNouM5yZgtYZIhRn3QO-Mdh5QsjSI0kAaLgJhtfJbaqJAiywInQkDfkUG5aaEG0JtpqNUIcNTXFgX6glPYRIL4KmHZSeH5LoxR_LdKlckaKgEDTUk46OBkm7R1kmEStUof89v_504Iic-ZJCYVY3iMRlsqx3ceVje2vvGOT-phI4B
  priority: 102
  providerName: Cornell University
Title Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model
URI https://www.proquest.com/docview/2083687453
https://arxiv.org/abs/0903.3146
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3PS8MwFA66IXjzt_MXOXiNtk3TJSdB2RRhU0Rht5Kkr1i03Wyn7OTfbl5W9SB4KbS5vaT53vfl5XuEnNrQGKEhYlzJgMXKBkzmgLWuxgrt8Ep4HXI0Tm6e4tuJmLSCW9OWVX7viX6jzqYWNXJH0iVP0JudX8zeGHaNwtPVtoXGKuk6oFa4quXw-kdjiZK-y5j58nTSW3ed63pRfJyhOOHYKma9Xf_lz07s4WW4Qbr3egb1JlmBaous-apM22yTpyXlh4yWelGU7yV9LV7gtUArYor2GCVS5oYWFdX0GQtbps0LZBq9lynUtRtjRcU-HB_GG1IN9X1vdsjjcPB4dcPaPghMi5CzEATXEJs80o7MgFEKIhnm3GUGVivrECjPgigLhOZxEBsl3T_Yl6DiPAitDgO-SzrVtIJ9Qk2uokwiJZQ8NjZUfZ5BP4mBZw7HreiRPR-OdLa0ukgxUCkGqkeOvgOUtqu8SX_n5OD_4UOy7hINgVpslByRzrx-h2MH5nNz4mfshHQvB-P7B_d2fTdxz9Hn4AtilaL2
linkProvider ProQuest
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT9wwELYoK1RufdBCS1sfytFt4kcSHxASLWgpsEJokbhFfkxEBJvdJkDpj-O_4fFmywGpN66xlMPYnpnv88w3hHx1qbXKAGdCFwmT2iWsqABrXa1TJsQrFXnI41E2PJO_ztX5Erlf9MJgWeXCJ0ZH7acOOfIA0guRoTa72Jn9Zjg1Cl9XFyM0TD9awW9HibG-seMQ_v4JEK7bPvgZ9nuL8_298Y8h66cMMKNSwVJQwoC0FTcBKoDVGniRViLEXWe0C_698gn3iTJCJtIGiF7keQFaVknqTJqI8NsXZCCF1AH7DXb3Rien_0genuUhZRfz59GoHfbdtHf17TdkRwJcxrR7EL88CQUxvu2_IoMTM4P2NVmC5g1ZiWWhrntLzuacA3g6MXf15GZCr-pLuKpRC5miPscEMXtH64YaeoGVNdPuErxB8WcKbRvWWN2w2wDIsUWro3HwzhoZP4eJ3pHlZtrAOqG20twXiEkLIa1LdS485JkE4UMi4dQGeR_NUc7mWhslGqpEQ22QzYWByv6adeXjofjw_-Uv5OVwfHxUHh2MDj-S1ZD1KCSGebZJlq_bG_gUMotr-7nfP0rKZz4xD1qo4Ks
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Improved+maximum+likelihood+estimators+in+a+heteroskedastic+errors-in-variables+model&rft.jtitle=arXiv.org&rft.au=Patriota%2C+Alexandre+G&rft.au=Lemonte%2C+Artur+J&rft.au=Bolfarine%2C+Heleno&rft.date=2015-08-26&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422&rft_id=info:doi/10.48550%2Farxiv.0903.3146