Identifiability of the random effects’ covariance matrix of the linear mixed model

Matteo Amestoy; Mark A. van de Wiel; Wessel N. van Wieringen

doi:https://doi.org/10.1080/03610926.2023.2272003

Identifiability of the random effects’ covariance matrix of the linear mixed model

Matteo Amestoy, Mark A. van de Wiel, Wessel N. van Wieringen

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Novel necessary and sufficient conditions for the identifiability of the linear mixed model are derived. These conditions either relax or generalize previously reported conditions. The novel conditions are translated to criteria that can be checked for most commonly employed parametrizations of the random effect’s covariance matrix of linear mixed model.

Original language	English
Journal	Communications in Statistics - Theory and Methods
Early online date	2023
DOIs	https://doi.org/10.1080/03610926.2023.2272003
Publication status	E-pub ahead of print - 2023

Keywords

Injectivity
non linear parametrization
normality

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https://doi.org/10.1080/03610926.2023.2272003

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title = "Identifiability of the random effects{\textquoteright} covariance matrix of the linear mixed model",

abstract = "Novel necessary and sufficient conditions for the identifiability of the linear mixed model are derived. These conditions either relax or generalize previously reported conditions. The novel conditions are translated to criteria that can be checked for most commonly employed parametrizations of the random effect{\textquoteright}s covariance matrix of linear mixed model.",

keywords = "Injectivity, non linear parametrization, normality",

author = "Matteo Amestoy and {van de Wiel}, {Mark A.} and {van Wieringen}, {Wessel N.}",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.",

year = "2023",

doi = "https://doi.org/10.1080/03610926.2023.2272003",

language = "English",

journal = "Communications in Statistics - Theory and Methods",

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T1 - Identifiability of the random effects’ covariance matrix of the linear mixed model

AU - Amestoy, Matteo

AU - van de Wiel, Mark A.

AU - van Wieringen, Wessel N.

PY - 2023

Y1 - 2023

N2 - Novel necessary and sufficient conditions for the identifiability of the linear mixed model are derived. These conditions either relax or generalize previously reported conditions. The novel conditions are translated to criteria that can be checked for most commonly employed parametrizations of the random effect’s covariance matrix of linear mixed model.

AB - Novel necessary and sufficient conditions for the identifiability of the linear mixed model are derived. These conditions either relax or generalize previously reported conditions. The novel conditions are translated to criteria that can be checked for most commonly employed parametrizations of the random effect’s covariance matrix of linear mixed model.

KW - Injectivity

KW - non linear parametrization

KW - normality

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U2 - https://doi.org/10.1080/03610926.2023.2272003

DO - https://doi.org/10.1080/03610926.2023.2272003

M3 - Article

SN - 0361-0926

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

ER -

Identifiability of the random effects’ covariance matrix of the linear mixed model

Abstract

Keywords

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