A comparative study of estimators in multilevel linear models

Multilevel Models are widely used in organizational research, educational research, epidemiology, psychology, biology and medical fields. In this paper, we recommend the situations where Bootstrap procedures through Minimum Norm Quadratic Unbiased Estimator (MINQUE) can be extremely handy than that...

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Detalles Bibliográficos
Autores principales: Ali, Sabz, Shah, Said Ali, Zubair, Seema, Hussain, Sundas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601462/
https://www.ncbi.nlm.nih.gov/pubmed/34793510
http://dx.doi.org/10.1371/journal.pone.0259960
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author Ali, Sabz
Shah, Said Ali
Zubair, Seema
Hussain, Sundas
author_facet Ali, Sabz
Shah, Said Ali
Zubair, Seema
Hussain, Sundas
author_sort Ali, Sabz
collection PubMed
description Multilevel Models are widely used in organizational research, educational research, epidemiology, psychology, biology and medical fields. In this paper, we recommend the situations where Bootstrap procedures through Minimum Norm Quadratic Unbiased Estimator (MINQUE) can be extremely handy than that of Restricted Maximum Likelihood (REML) in multilevel level linear regression models. In our simulation study the bootstrap by means of MINQUE is superior to REML in conditions where normality does not hold. Moreover, the real data application also supports our findings in terms of accuracy of estimates and their standard errors.
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spelling pubmed-86014622021-11-19 A comparative study of estimators in multilevel linear models Ali, Sabz Shah, Said Ali Zubair, Seema Hussain, Sundas PLoS One Research Article Multilevel Models are widely used in organizational research, educational research, epidemiology, psychology, biology and medical fields. In this paper, we recommend the situations where Bootstrap procedures through Minimum Norm Quadratic Unbiased Estimator (MINQUE) can be extremely handy than that of Restricted Maximum Likelihood (REML) in multilevel level linear regression models. In our simulation study the bootstrap by means of MINQUE is superior to REML in conditions where normality does not hold. Moreover, the real data application also supports our findings in terms of accuracy of estimates and their standard errors. Public Library of Science 2021-11-18 /pmc/articles/PMC8601462/ /pubmed/34793510 http://dx.doi.org/10.1371/journal.pone.0259960 Text en © 2021 Ali et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ali, Sabz
Shah, Said Ali
Zubair, Seema
Hussain, Sundas
A comparative study of estimators in multilevel linear models
title A comparative study of estimators in multilevel linear models
title_full A comparative study of estimators in multilevel linear models
title_fullStr A comparative study of estimators in multilevel linear models
title_full_unstemmed A comparative study of estimators in multilevel linear models
title_short A comparative study of estimators in multilevel linear models
title_sort comparative study of estimators in multilevel linear models
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601462/
https://www.ncbi.nlm.nih.gov/pubmed/34793510
http://dx.doi.org/10.1371/journal.pone.0259960
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