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On non-negative estimation of variance components in mixed linear models

Alternative estimators have been derived for estimating the variance components according to Iterative Almost Unbiased Estimation (IAUE). As a result two modified IAUEs are introduced. The relative performances of the proposed estimators and other estimators are studied by simulating their bias, Mea...

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Detalles Bibliográficos
Autores principales: El Leithy, Heba A., Abdel Wahed, Zakaria A., Abdallah, Mohamed S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4703422/
https://www.ncbi.nlm.nih.gov/pubmed/26843971
http://dx.doi.org/10.1016/j.jare.2015.02.001
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author El Leithy, Heba A.
Abdel Wahed, Zakaria A.
Abdallah, Mohamed S.
author_facet El Leithy, Heba A.
Abdel Wahed, Zakaria A.
Abdallah, Mohamed S.
author_sort El Leithy, Heba A.
collection PubMed
description Alternative estimators have been derived for estimating the variance components according to Iterative Almost Unbiased Estimation (IAUE). As a result two modified IAUEs are introduced. The relative performances of the proposed estimators and other estimators are studied by simulating their bias, Mean Square Error and the probability of getting negative estimates under unbalanced nested-factorial model with two fixed crossed factorial and one nested random factor. Finally the Empirical Quantile Dispersion Graph (EQDG), which provides a comprehensive picture of the quality of estimation, is depicted corresponding to all the studied methods.
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spelling pubmed-47034222016-02-03 On non-negative estimation of variance components in mixed linear models El Leithy, Heba A. Abdel Wahed, Zakaria A. Abdallah, Mohamed S. J Adv Res Original Article Alternative estimators have been derived for estimating the variance components according to Iterative Almost Unbiased Estimation (IAUE). As a result two modified IAUEs are introduced. The relative performances of the proposed estimators and other estimators are studied by simulating their bias, Mean Square Error and the probability of getting negative estimates under unbalanced nested-factorial model with two fixed crossed factorial and one nested random factor. Finally the Empirical Quantile Dispersion Graph (EQDG), which provides a comprehensive picture of the quality of estimation, is depicted corresponding to all the studied methods. Elsevier 2016-01 2015-03-19 /pmc/articles/PMC4703422/ /pubmed/26843971 http://dx.doi.org/10.1016/j.jare.2015.02.001 Text en © 2015 Production and hosting by Elsevier B.V. on behalf of Cairo University. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Article
El Leithy, Heba A.
Abdel Wahed, Zakaria A.
Abdallah, Mohamed S.
On non-negative estimation of variance components in mixed linear models
title On non-negative estimation of variance components in mixed linear models
title_full On non-negative estimation of variance components in mixed linear models
title_fullStr On non-negative estimation of variance components in mixed linear models
title_full_unstemmed On non-negative estimation of variance components in mixed linear models
title_short On non-negative estimation of variance components in mixed linear models
title_sort on non-negative estimation of variance components in mixed linear models
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4703422/
https://www.ncbi.nlm.nih.gov/pubmed/26843971
http://dx.doi.org/10.1016/j.jare.2015.02.001
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