Improved best linear unbiased estimators for the simple linear regression model using double ranked set sampling schemes

In this paper, we consider the best linear unbiased estimators (BLUEs) based on double ranked set sampling (DRSS) and ordered DRSS (ODRSS) schemes for the simple linear regression model with replicated observations. We assume three symmetric distributions for the random error term, i.e., normal, Lap...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Communications in statistics. Theory and methods Ročník 45; číslo 12; s. 3541 - 3561
Hlavní autoři: Haq, Abdul, Brown, Jennifer, Moltchanova, Elena
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 17.06.2016
Taylor & Francis Ltd
Témata:
Best linear unbiased estimator
Economic models
Estimators
Mathematical models
Normality
Order statistics
Primary: 62D05; Secondary: 62F10
Ranked set sample
Regression
Regression analysis
Relative efficiency
Sampling
Sensitivity analysis
Simple linear regression model
Statistics
Symmetry
ISSN:0361-0926, 1532-415X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract In this paper, we consider the best linear unbiased estimators (BLUEs) based on double ranked set sampling (DRSS) and ordered DRSS (ODRSS) schemes for the simple linear regression model with replicated observations. We assume three symmetric distributions for the random error term, i.e., normal, Laplace and some scale contaminated normal distributions. The proposed BLUEs under DRSS (BLUEs-DRSS) and ODRSS (BLUEs-ODRSS) are compared with the BLUEs based on ordered simple random sampling (OSRS), ranked set sampling (RSS), and ordered RSS (ORSS) schemes. These estimators are compared in terms of relative efficiency (RE), RE of determinant (RED), and RE of trace (RET). It is found that the BLUEs-ODRSS are uniformly better than the BLUEs based on OSRS, RSS, ORSS, and DRSS schemes. We also compare the estimators based on imperfect RSS (IRSS) schemes. It is worth mentioning here that the BLUEs under ordered imperfect DRSS (OIDRSS) are better than their counterparts based on IRSS, ordered IRSS (OIRSS), and imperfect DRSS (IDRSS) methods. Moreover, for sensitivity analysis of the BLUEs, we calculate REs and REDs of the BLUEs under the assumption of normality when in fact the parent distribution follows a non normal symmetric distribution. It turns out that even under violation of normality assumptions, BLUEs of the intercept and the slope parameters are found to be unbiased with equal REs under each sampling scheme. It is also observed that the BLUEs under ODRSS are more efficient than the existing BLUEs.
AbstractList In this paper, we consider the best linear unbiased estimators (BLUEs) based on double ranked set sampling (DRSS) and ordered DRSS (ODRSS) schemes for the simple linear regression model with replicated observations. We assume three symmetric distributions for the random error term, i.e., normal, Laplace and some scale contaminated normal distributions. The proposed BLUEs under DRSS (BLUEs-DRSS) and ODRSS (BLUEs-ODRSS) are compared with the BLUEs based on ordered simple random sampling (OSRS), ranked set sampling (RSS), and ordered RSS (ORSS) schemes. These estimators are compared in terms of relative efficiency (RE), RE of determinant (RED), and RE of trace (RET). It is found that the BLUEs-ODRSS are uniformly better than the BLUEs based on OSRS, RSS, ORSS, and DRSS schemes. We also compare the estimators based on imperfect RSS (IRSS) schemes. It is worth mentioning here that the BLUEs under ordered imperfect DRSS (OIDRSS) are better than their counterparts based on IRSS, ordered IRSS (OIRSS), and imperfect DRSS (IDRSS) methods. Moreover, for sensitivity analysis of the BLUEs, we calculate REs and REDs of the BLUEs under the assumption of normality when in fact the parent distribution follows a non normal symmetric distribution. It turns out that even under violation of normality assumptions, BLUEs of the intercept and the slope parameters are found to be unbiased with equal REs under each sampling scheme. It is also observed that the BLUEs under ODRSS are more efficient than the existing BLUEs.
Author Brown, Jennifer
Moltchanova, Elena
Haq, Abdul
Author_xml – sequence: 1
  givenname: Abdul
  surname: Haq
  fullname: Haq, Abdul
  email: aaabdulhaq@yahoo.com
  organization: Department of Statistics, Quaid-i-Azam University
– sequence: 2
  givenname: Jennifer
  surname: Brown
  fullname: Brown, Jennifer
  organization: Department of Mathematics and Statistics, University of Canterbury
– sequence: 3
  givenname: Elena
  surname: Moltchanova
  fullname: Moltchanova, Elena
  organization: Department of Mathematics and Statistics, University of Canterbury
BookMark eNqFkUFv1DAQhS1UJLaFf8DBEhcuWcax4zhcEKqgrVSJC0jcLMd2WhfHXjxJof8eR0svPcDJ0vh7TzPvnZKTlJMn5DWDPQMF74BLBkMr9y0wsR9A8A6ekR3reNsI1n0_IbsNaTbmBTlFvANgXa_4jvy-mg8l33tHR48LjSF5U-iaxmCwDusszGbJBemUC11uPcUwH6J_JIu_KR4x5ETn7HykK4Z0Q11exwoVk35UF_QLRVNl2xfaWz97fEmeTyaif_X3PSPfPn_6en7ZXH-5uDr_eN1YLtXSyIlxI8A52fZyMIODsbee2cEyLnrLjXGt4j234AY7tMxOHKQblbQM2DhM_Iy8PfrWM3-u9R49B7Q-RpN8XlEzxSTwTkBb0TdP0Lu8llS306xXUoFQvaiUOFK2ZMTiJ30oNaPyoBnorQ79WIfe6tDHOqrs_ROZDYtZanBLMSH-T_zhKA6p1jCbX7lEpxfzEHOZasg2oOb_dPgDUBGmZA
CitedBy_id crossref_primary_10_1007_s00362_025_01707_9
crossref_primary_10_1080_00949655_2018_1444040
crossref_primary_10_1080_00949655_2019_1665043
crossref_primary_10_32604_cmc_2021_015047
Cites_doi 10.1002/bimj.4710370704
10.1007/BF02911622
10.5209/rev_REMA.2007.v20.n1.16528
10.1093/biomet/39.1-2.88
10.1080/00401706.1974.10489214
10.1023/A:1009609902784
10.1111/j.2517-6161.1972.tb00909.x
10.2307/2556166
10.1080/00949650701756872
10.1002/0471722162
10.1071/AR9520385
10.1016/S0167-7152(99)00206-0
10.2307/2556187
10.1081/SAC-200055641
10.1016/j.jspi.2005.08.050
ContentType Journal Article
Copyright 2016 Taylor & Francis Group, LLC 2016
2016 Taylor & Francis Group, LLC
Copyright_xml – notice: 2016 Taylor & Francis Group, LLC 2016
– notice: 2016 Taylor & Francis Group, LLC
DBID AAYXX
CITATION
7SC
7TB
8FD
FR3
JQ2
KR7
L7M
L~C
L~D
DOI 10.1080/03610926.2014.904350
DatabaseName CrossRef
Computer and Information Systems Abstracts
Mechanical & Transportation Engineering Abstracts
Technology Research Database
Engineering Research Database
ProQuest Computer Science Collection
Civil Engineering Abstracts
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
DatabaseTitle CrossRef
Civil Engineering Abstracts
Technology Research Database
Computer and Information Systems Abstracts – Academic
Mechanical & Transportation Engineering Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts Professional
DatabaseTitleList Civil Engineering Abstracts
Civil Engineering Abstracts
DeliveryMethod fulltext_linktorsrc
Discipline Statistics
Mathematics
EISSN 1532-415X
EndPage 3561
ExternalDocumentID 4044968801
10_1080_03610926_2014_904350
904350
Genre Article
Feature
GroupedDBID -~X
.7F
.QJ
0BK
0R~
29F
2DF
30N
4.4
5GY
5VS
8VB
AAENE
AAGDL
AAHIA
AAJMT
AALDU
AAMIU
AAPUL
AAQRR
ABCCY
ABEHJ
ABFIM
ABHAV
ABJNI
ABLIJ
ABPAQ
ABPEM
ABTAI
ABXUL
ABXYU
ACGEJ
ACGFS
ACIWK
ACTIO
ADCVX
ADGTB
ADXPE
AEISY
AEOZL
AEPSL
AEYOC
AFKVX
AFRVT
AGDLA
AGMYJ
AIJEM
AIYEW
AJWEG
AKBVH
AKOOK
ALMA_UNASSIGNED_HOLDINGS
ALQZU
AMVHM
AQRUH
AQTUD
AVBZW
AWYRJ
BLEHA
CCCUG
CE4
CS3
DGEBU
DKSSO
EBS
EJD
E~A
E~B
F5P
GTTXZ
H13
HF~
HZ~
H~P
IPNFZ
J.P
K1G
KYCEM
M4Z
NA5
NY~
O9-
QWB
RIG
RNANH
ROSJB
RTWRZ
S-T
SNACF
TASJS
TBQAZ
TDBHL
TEJ
TFL
TFT
TFW
TN5
TOXWX
TTHFI
TUROJ
TWF
TWZ
UPT
UT5
UU3
WH7
ZGOLN
ZL0
~02
~S~
AAYXX
CITATION
7SC
7TB
8FD
FR3
JQ2
KR7
L7M
L~C
L~D
ID FETCH-LOGICAL-c368t-6f13a40dd62769a9d0b7ce1c9c1347c3aad28373c0d9c921cf306db86c101b9f3
IEDL.DBID TFW
ISICitedReferencesCount 4
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000375864900010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0361-0926
IngestDate Sun Nov 09 11:08:56 EST 2025
Wed Aug 13 03:43:16 EDT 2025
Sat Nov 29 01:53:49 EST 2025
Tue Nov 18 22:35:54 EST 2025
Mon Oct 20 23:47:23 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 12
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c368t-6f13a40dd62769a9d0b7ce1c9c1347c3aad28373c0d9c921cf306db86c101b9f3
Notes SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
PQID 1786804874
PQPubID 186202
PageCount 21
ParticipantIDs crossref_primary_10_1080_03610926_2014_904350
crossref_citationtrail_10_1080_03610926_2014_904350
informaworld_taylorfrancis_310_1080_03610926_2014_904350
proquest_miscellaneous_1816035402
proquest_journals_1786804874
PublicationCentury 2000
PublicationDate 2016-06-17
PublicationDateYYYYMMDD 2016-06-17
PublicationDate_xml – month: 06
  year: 2016
  text: 2016-06-17
  day: 17
PublicationDecade 2010
PublicationPlace Philadelphia
PublicationPlace_xml – name: Philadelphia
PublicationTitle Communications in statistics. Theory and methods
PublicationYear 2016
Publisher Taylor & Francis
Taylor & Francis Ltd
Publisher_xml – name: Taylor & Francis
– name: Taylor & Francis Ltd
References cit0011
Vaughan R.J. (cit0017) 1972; 34
cit0001
cit0012
Leone F.C. (cit0010) 1973; 68
Bapat R.B. (cit0005) 1989; 1
cit0008
cit0009
cit0006
cit0007
cit0004
cit0015
cit0016
cit0002
cit0013
cit0003
cit0014
References_xml – ident: cit0015
  doi: 10.1002/bimj.4710370704
– ident: cit0016
  doi: 10.1007/BF02911622
– ident: cit0002
  doi: 10.5209/rev_REMA.2007.v20.n1.16528
– volume: 68
  start-page: 953
  year: 1973
  ident: cit0010
  publication-title: J. Am. Stat. Assoc.
– ident: cit0012
  doi: 10.1093/biomet/39.1-2.88
– volume: 1
  start-page: 79
  year: 1989
  ident: cit0005
  publication-title: Sankhya, Series A
– ident: cit0014
  doi: 10.1080/00401706.1974.10489214
– ident: cit0006
  doi: 10.1023/A:1009609902784
– volume: 34
  start-page: 308
  year: 1972
  ident: cit0017
  publication-title: J. R. Stat. Soc. Ser. B (Methodol.)
  doi: 10.1111/j.2517-6161.1972.tb00909.x
– ident: cit0009
  doi: 10.2307/2556166
– ident: cit0011
  doi: 10.1080/00949650701756872
– ident: cit0008
  doi: 10.1002/0471722162
– ident: cit0013
  doi: 10.1071/AR9520385
– ident: cit0001
  doi: 10.1016/S0167-7152(99)00206-0
– ident: cit0007
  doi: 10.2307/2556187
– ident: cit0003
  doi: 10.1081/SAC-200055641
– ident: cit0004
  doi: 10.1016/j.jspi.2005.08.050
SSID ssj0015783
Score 2.0819745
Snippet In this paper, we consider the best linear unbiased estimators (BLUEs) based on double ranked set sampling (DRSS) and ordered DRSS (ODRSS) schemes for the...
SourceID proquest
crossref
informaworld
SourceType Aggregation Database
Enrichment Source
Index Database
Publisher
StartPage 3541
SubjectTerms Best linear unbiased estimator
Economic models
Estimators
Mathematical models
Normality
Order statistics
Primary: 62D05; Secondary: 62F10
Ranked set sample
Regression
Regression analysis
Relative efficiency
Sampling
Sensitivity analysis
Simple linear regression model
Statistics
Symmetry
Title Improved best linear unbiased estimators for the simple linear regression model using double ranked set sampling schemes
URI https://www.tandfonline.com/doi/abs/10.1080/03610926.2014.904350
https://www.proquest.com/docview/1786804874
https://www.proquest.com/docview/1816035402
Volume 45
WOSCitedRecordID wos000375864900010&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAWR
  databaseName: Taylor & Francis Online Journals
  customDbUrl:
  eissn: 1532-415X
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0015783
  issn: 0361-0926
  databaseCode: TFW
  dateStart: 19760101
  isFulltext: true
  titleUrlDefault: https://www.tandfonline.com
  providerName: Taylor & Francis
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEA4iHvTgY1VcX0TwWm2abtIcRVy8KB5W9Fby6rKgXdnuij_fmaZddhEV9FgyeTSZzCOZfEPIOeg0ZVXqImF7KTgoBY90lujIgzyOrZJGm7RONiHv77PnZ_Ww8IofwyrRhy4CUEQtq3Fza1O1EXGXIHRZrBIMMGDphYpB46PTDpofd-ag_zS_RgB2DPmRBfjMUKN9O_dNI0u6aQm59IukrtVPf-v_A98mm43pSa8Cr-yQFV92yMbdHLe16pB1tD0DdPMu-QgHDt5RAwOi2J-e0FlpRqD5HEV4jld02SsKf0ChFVqNEGu4pZz4YQiyLWmdb4dijP2QuvHMABEmi4dWKj-llcawdigCT9u_-mqPPPZvBte3UZOoIbJcZNNIFIzrNHZOJFIorVxspPXMKosPVS3X2iHIDrexA8ZImC3AUXEmExYEglEF3yer5bj0B4QK5nRPcOa95al13hhuwcjAuSqkl7pLeLtEuW1QzDGZxkvOWrDTZpJznOQ8THKXRPNabwHF4xf6bHH182l9elKEVCc5_7nqccspeSMOqpzJTGQgK2XaJWfzYtjIeDujSz-eAU2GGb_BgE4O_977EVmHL4GxbEwek9XpZOZPyJp9B96ZnNab4xMKLQxo
linkProvider Taylor & Francis
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3dTxQxEJ8QJBEeQFHDIWpNfF3dbvfa7aMxXiDAxYcz8tb0awmJ7JHbO8Kfz8x29yIxSmJ87rTdnU7no53-BuAD2jTtdRky6cclBii1yGxV2CyiPs69Vs66sis2oabT6uJCf-uzCds-rZJi6DoBRXS6mjY3HUYPKXGfUOvyXBeUYcDLjzpHk49R-5MxmlqCz59NfqwvElAgU4VkiVEzdhlez_1hlAfW6QF26W-6ujNAk73_8OnPYLf3PtnnJC7PYSM2-7BzvoZubfdhm9zPhN78Au7SmUMMzOEXMZrQLtiqcVdo_AIjhI5ritpbhr_AcBTWXhHc8EC5iJcpz7ZhXckdRmn2lyzMVw6JqF48jtLGJWstZbZjEwbb8Tq2L-H75Ovsy3HW12rIvJDVMpM1F7bMQ5CFktrqkDvlI_fa01tVL6wNhLMjfB5QNgrua4xVgqukR53gdC1ewWYzb-IBMMmDHUvBY_Si9CE6Jzz6GcSrWkVlRyCGNTK-BzKneho_DR_wTnsmG2KySUweQbbudZOAPB6hr35dfrPsDlDqVO3EiL93PRpExfQaoTVcVbJCdanKEbxfN-Nepgsa28T5CmkqKvqNPnRx-O-zv4Onx7PzM3N2Mj19DdvYIim1jasj2FwuVvENbPlblKPF226n3AP_6RCS
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3dTxQxEJ8YNAYfFFDCKWJJfF3dbvfa7aMRLxD1wgNG3pp-LSGBvcvtneHPZ2a7e4EQNdHnTtvd6XQ-2ulvAN6jTdNelyGTflxigFKLzFaFzSLq49xr5awru2ITajqtzs_16Z1X_JRWSTF0nYAiOl1Nm3se6iEj7iMqXZ7rghIMePlB52jxMWh_jJ6zJBk_m_xc3yOgPKYCyRKDZuwyPJ77zSj3jNM96NIHqrqzP5MX___lW_C89z3ZpyQs2_AoNjvw7PsauLXdgU1yPhN280u4SScOMTCHH8RoPrtgq8ZdoukLjPA5rilmbxn-AcNRWHtJYMMD5SJepCzbhnUFdxgl2V-wMFs5JKJq8ThKG5estZTXjk0Yasfr2L6CH5MvZ5-Ps75SQ-aFrJaZrLmwZR6CLJTUVofcKR-5155eqnphbSCUHeHzgJJRcF9jpBJcJT1qBKdrsQsbzayJe8AkD3YsBY_Ri9KH6Jzw6GUQr2oVlR2BGJbI-B7GnKppXBk-oJ32TDbEZJOYPIJs3WueYDz-Ql_dXX2z7I5P6lTrxIg_d90fJMX0-qA1XFWyQmWpyhEcrptxJ9P1jG3ibIU0FZX8Rg-6eP3vs7-Dp6dHE_PtZPr1DWxig6S8Nq72YWO5WMW38MT_QjFaHHT75Ba62w9E
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+best+linear+unbiased+estimators+for+the+simple+linear+regression+model+using+double+ranked+set+sampling+schemes&rft.jtitle=Communications+in+statistics.+Theory+and+methods&rft.au=Haq%2C+Abdul&rft.au=Brown%2C+Jennifer&rft.au=Moltchanova%2C+Elena&rft.date=2016-06-17&rft.issn=0361-0926&rft.eissn=1532-415X&rft.volume=45&rft.issue=12&rft.spage=3541&rft.epage=3561&rft_id=info:doi/10.1080%2F03610926.2014.904350&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0361-0926&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0361-0926&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0361-0926&client=summon