Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing

In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecificatio...
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Autor*in:	Shakeel Ahmed [verfasserIn] Javid Shabbir [verfasserIn] Huda M. Alshanbari [verfasserIn] Safoora Anjum [verfasserIn] Abd AL-Aziz Hosni EL-Bagoury [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function

Übergeordnetes Werk:	In: Heliyon - Elsevier, 2016, 10(2024), 3, Seite e25106-
Übergeordnetes Werk:	volume:10 ; year:2024 ; number:3 ; pages:e25106-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.heliyon.2024.e25106

Katalog-ID:	DOAJ101376715

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520			\|a In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure.
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allfields	10.1016/j.heliyon.2024.e25106 doi (DE-627)DOAJ101376715 (DE-599)DOAJ95b9c2d1bca445e0af9efdcc795e55ff DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Shakeel Ahmed verfasserin aut Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure. Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function Science (General) Social sciences (General) Javid Shabbir verfasserin aut Huda M. Alshanbari verfasserin aut Safoora Anjum verfasserin aut Abd AL-Aziz Hosni EL-Bagoury verfasserin aut In Heliyon Elsevier, 2016 10(2024), 3, Seite e25106- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:10 year:2024 number:3 pages:e25106- https://doi.org/10.1016/j.heliyon.2024.e25106 kostenfrei https://doaj.org/article/95b9c2d1bca445e0af9efdcc795e55ff kostenfrei http://www.sciencedirect.com/science/article/pii/S240584402401137X kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2024 3 e25106-
spelling	10.1016/j.heliyon.2024.e25106 doi (DE-627)DOAJ101376715 (DE-599)DOAJ95b9c2d1bca445e0af9efdcc795e55ff DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Shakeel Ahmed verfasserin aut Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure. Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function Science (General) Social sciences (General) Javid Shabbir verfasserin aut Huda M. Alshanbari verfasserin aut Safoora Anjum verfasserin aut Abd AL-Aziz Hosni EL-Bagoury verfasserin aut In Heliyon Elsevier, 2016 10(2024), 3, Seite e25106- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:10 year:2024 number:3 pages:e25106- https://doi.org/10.1016/j.heliyon.2024.e25106 kostenfrei https://doaj.org/article/95b9c2d1bca445e0af9efdcc795e55ff kostenfrei http://www.sciencedirect.com/science/article/pii/S240584402401137X kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2024 3 e25106-
allfields_unstemmed	10.1016/j.heliyon.2024.e25106 doi (DE-627)DOAJ101376715 (DE-599)DOAJ95b9c2d1bca445e0af9efdcc795e55ff DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Shakeel Ahmed verfasserin aut Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure. Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function Science (General) Social sciences (General) Javid Shabbir verfasserin aut Huda M. Alshanbari verfasserin aut Safoora Anjum verfasserin aut Abd AL-Aziz Hosni EL-Bagoury verfasserin aut In Heliyon Elsevier, 2016 10(2024), 3, Seite e25106- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:10 year:2024 number:3 pages:e25106- https://doi.org/10.1016/j.heliyon.2024.e25106 kostenfrei https://doaj.org/article/95b9c2d1bca445e0af9efdcc795e55ff kostenfrei http://www.sciencedirect.com/science/article/pii/S240584402401137X kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2024 3 e25106-
allfieldsGer	10.1016/j.heliyon.2024.e25106 doi (DE-627)DOAJ101376715 (DE-599)DOAJ95b9c2d1bca445e0af9efdcc795e55ff DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Shakeel Ahmed verfasserin aut Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure. Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function Science (General) Social sciences (General) Javid Shabbir verfasserin aut Huda M. Alshanbari verfasserin aut Safoora Anjum verfasserin aut Abd AL-Aziz Hosni EL-Bagoury verfasserin aut In Heliyon Elsevier, 2016 10(2024), 3, Seite e25106- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:10 year:2024 number:3 pages:e25106- https://doi.org/10.1016/j.heliyon.2024.e25106 kostenfrei https://doaj.org/article/95b9c2d1bca445e0af9efdcc795e55ff kostenfrei http://www.sciencedirect.com/science/article/pii/S240584402401137X kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2024 3 e25106-
allfieldsSound	10.1016/j.heliyon.2024.e25106 doi (DE-627)DOAJ101376715 (DE-599)DOAJ95b9c2d1bca445e0af9efdcc795e55ff DE-627 ger DE-627 rakwb eng Q1-390 H1-99 Shakeel Ahmed verfasserin aut Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure. Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function Science (General) Social sciences (General) Javid Shabbir verfasserin aut Huda M. Alshanbari verfasserin aut Safoora Anjum verfasserin aut Abd AL-Aziz Hosni EL-Bagoury verfasserin aut In Heliyon Elsevier, 2016 10(2024), 3, Seite e25106- (DE-627)835893197 (DE-600)2835763-2 24058440 nnns volume:10 year:2024 number:3 pages:e25106- https://doi.org/10.1016/j.heliyon.2024.e25106 kostenfrei https://doaj.org/article/95b9c2d1bca445e0af9efdcc795e55ff kostenfrei http://www.sciencedirect.com/science/article/pii/S240584402401137X kostenfrei https://doaj.org/toc/2405-8440 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_171 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 10 2024 3 e25106-
language	English
source	In Heliyon 10(2024), 3, Seite e25106- volume:10 year:2024 number:3 pages:e25106-
sourceStr	In Heliyon 10(2024), 3, Seite e25106- volume:10 year:2024 number:3 pages:e25106-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function Science (General) Social sciences (General)
isfreeaccess_bool	true
container_title	Heliyon
authorswithroles_txt_mv	Shakeel Ahmed @@aut@@ Javid Shabbir @@aut@@ Huda M. Alshanbari @@aut@@ Safoora Anjum @@aut@@ Abd AL-Aziz Hosni EL-Bagoury @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	835893197
id	DOAJ101376715
language_de	englisch
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callnumber-first	Q - Science
author	Shakeel Ahmed
spellingShingle	Shakeel Ahmed misc Q1-390 misc H1-99 misc Model-based approach misc Superpopulation misc Efficiency improvement misc Parameter estimation misc Basis function misc Science (General) misc Social sciences (General) Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing
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topic_title	Q1-390 H1-99 Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing Model-based approach Superpopulation Efficiency improvement Parameter estimation Basis function
topic	misc Q1-390 misc H1-99 misc Model-based approach misc Superpopulation misc Efficiency improvement misc Parameter estimation misc Basis function misc Science (General) misc Social sciences (General)
topic_unstemmed	misc Q1-390 misc H1-99 misc Model-based approach misc Superpopulation misc Efficiency improvement misc Parameter estimation misc Basis function misc Science (General) misc Social sciences (General)
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title	Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing
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title_full	Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing
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author_browse	Shakeel Ahmed Javid Shabbir Huda M. Alshanbari Safoora Anjum Abd AL-Aziz Hosni EL-Bagoury
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title_sort	adjustment of model misspecification in estimation of population total under ranked set sampling through balancing
callnumber	Q1-390
title_auth	Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing
abstract	In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure.
abstractGer	In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure.
abstract_unstemmed	In the model-based approach, researchers assume that the underlying structure, which generates the population of interest, is correctly specified. However, when the working model differs from the underlying true population model, the estimation process becomes quite unreliable due to misspecification bias. Selecting a sample by applying the balancing conditions on some functions of the covariates can reduce such bias. This study aims at suggesting an estimator of population total by applying the balancing conditions on the basis functions of the auxiliary character(s) for the situations where the working model is different from the underlying true model under a ranked set sampling without replacement scheme. Special cases of the misspecified basis function model, i.e. homogeneous, linear, and proportional, are considered and balancing conditions are introduced in each case. Both simulation and bootstrapped studies show that the total estimators under proposed sampling mechanism keep up the superiority over simple random sampling in terms of efficiency and maintaining robustness against model failure.
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title_short	Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing
url	https://doi.org/10.1016/j.heliyon.2024.e25106 https://doaj.org/article/95b9c2d1bca445e0af9efdcc795e55ff http://www.sciencedirect.com/science/article/pii/S240584402401137X https://doaj.org/toc/2405-8440
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author2	Javid Shabbir Huda M. Alshanbari Safoora Anjum Abd AL-Aziz Hosni EL-Bagoury
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Adjustment of model misspecification in estimation of population total under ranked set sampling through balancing

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