Assessing skewness, kurtosis and normality in linear mixedmodels

ABSTRACT: Linear mixed models provide a useful tool to fit continuous longitudinal data, with the random effects and error term commonly assumed to have normal distributions. However, this restrictive assumption can result in a lack of robustness and needs to be tested. In this paper, we propose tes...

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Detalhes bibliográficos
Autores: Soberón Velez, Alexandra Pilar|||0000-0001-5268-6751, Stute, Winfried
Formato: artículo
Fecha de publicación:2017
País:España
Recursos:Universidad de Cantabria (UC)
Repositorio:UCrea Repositorio Abierto de la Universidad de Cantabria
Idioma:inglés
OAI Identifier:oai:repositorio.unican.es:10902/12965
Acesso em linha:http://hdl.handle.net/10902/12965
Access Level:acceso abierto
Palavra-chave:Kurtosis
Linear mixed model
Longitudinal data
Moment estimator
Normality
Skewness.
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spelling Assessing skewness, kurtosis and normality in linear mixedmodelsSoberón Velez, Alexandra Pilar|||0000-0001-5268-6751 Stute, WinfriedKurtosisLinear mixed modelLongitudinal dataMoment estimatorNormalitySkewness.ABSTRACT: Linear mixed models provide a useful tool to fit continuous longitudinal data, with the random effects and error term commonly assumed to have normal distributions. However, this restrictive assumption can result in a lack of robustness and needs to be tested. In this paper, we propose tests for skewness, kurtosis, and normality based on generalized least squares (GLS) residuals. To do it, estimating higher order moments is necessary and an alternative estimation procedure is developed. Compared to other procedures in the literature, our approach provides a closed form expression even for the third and fourth order moments. In addition, no further distributional assumptions on either random effects or error terms are needed to show the consistency of the proposed estimators and tests statistics. Their finite-sample performance is examined in a Monte Carlo study and the methodology is used to examine changes in the life expectancy as well as maternal and infant mortality rate of a sample of OECD countries.Academic Press Inc.Universidad de Cantabria20172017-09-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/10902/12965Journal of Multivariate Analysis 161 (2017) 123-140reponame:UCrea Repositorio Abierto de la Universidad de Cantabriainstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.unican.es:10902/129652026-06-02T12:39:31Z
dc.title.none.fl_str_mv Assessing skewness, kurtosis and normality in linear mixedmodels
title Assessing skewness, kurtosis and normality in linear mixedmodels
spellingShingle Assessing skewness, kurtosis and normality in linear mixedmodels
Soberón Velez, Alexandra Pilar|||0000-0001-5268-6751
Kurtosis
Linear mixed model
Longitudinal data
Moment estimator
Normality
Skewness.
title_short Assessing skewness, kurtosis and normality in linear mixedmodels
title_full Assessing skewness, kurtosis and normality in linear mixedmodels
title_fullStr Assessing skewness, kurtosis and normality in linear mixedmodels
title_full_unstemmed Assessing skewness, kurtosis and normality in linear mixedmodels
title_sort Assessing skewness, kurtosis and normality in linear mixedmodels
dc.creator.none.fl_str_mv Soberón Velez, Alexandra Pilar|||0000-0001-5268-6751
Stute, Winfried
author Soberón Velez, Alexandra Pilar|||0000-0001-5268-6751
author_facet Soberón Velez, Alexandra Pilar|||0000-0001-5268-6751
Stute, Winfried
author_role author
author2 Stute, Winfried
author2_role author
dc.contributor.none.fl_str_mv Universidad de Cantabria
dc.subject.none.fl_str_mv Kurtosis
Linear mixed model
Longitudinal data
Moment estimator
Normality
Skewness.
topic Kurtosis
Linear mixed model
Longitudinal data
Moment estimator
Normality
Skewness.
description ABSTRACT: Linear mixed models provide a useful tool to fit continuous longitudinal data, with the random effects and error term commonly assumed to have normal distributions. However, this restrictive assumption can result in a lack of robustness and needs to be tested. In this paper, we propose tests for skewness, kurtosis, and normality based on generalized least squares (GLS) residuals. To do it, estimating higher order moments is necessary and an alternative estimation procedure is developed. Compared to other procedures in the literature, our approach provides a closed form expression even for the third and fourth order moments. In addition, no further distributional assumptions on either random effects or error terms are needed to show the consistency of the proposed estimators and tests statistics. Their finite-sample performance is examined in a Monte Carlo study and the methodology is used to examine changes in the life expectancy as well as maternal and infant mortality rate of a sample of OECD countries.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10902/12965
url http://hdl.handle.net/10902/12965
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Academic Press Inc.
publisher.none.fl_str_mv Academic Press Inc.
dc.source.none.fl_str_mv Journal of Multivariate Analysis 161 (2017) 123-140
reponame:UCrea Repositorio Abierto de la Universidad de Cantabria
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str UCrea Repositorio Abierto de la Universidad de Cantabria
collection UCrea Repositorio Abierto de la Universidad de Cantabria
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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