Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models
Author(s) / Creator(s)
Mason, Fabio
Cantoni, Eva
Ghisletta, Paolo
Abstract / Description
The linear mixed model (LMM) is a popular statistical model for the analysis of longitudinal data. However, the robust estimation of and inferential conclusions for the LMM in the presence of outliers (i.e., observations with very low probability of occurrence under Normality) is not part of mainstream longitudinal data analysis. In this work, we compared the coverage rates of confidence intervals (CIs) based on two bootstrap methods, applied to three robust estimation methods. We carried out a simulation experiment to compare CIs under three different conditions: data 1) without contamination, 2) contaminated by within-, or 3) between-participant outliers. Results showed that the semi-parametric bootstrap associated to the composite tau-estimator leads to valid inferential decisions with both uncontaminated and contaminated data. This being the most comprehensive study of CIs applied to robust estimators of the LMM, we provide fully commented R code for all methods applied to a popular example.
Keyword(s)
robustness linear mixed models bootstrap confidence intervals longitudinal dataPersistent Identifier
Date of first publication
2021-12-17
Journal title
Methodology
Volume
17
Issue
4
Page numbers
271–295
Publisher
PsychOpen GOLD
Publication status
publishedVersion
Review status
peerReviewed
Is version of
Citation
Mason, F., Cantoni, E., & Ghisletta, P. (2021). Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models. Methodology, 17(4), 271-295. https://doi.org/10.5964/meth.6607
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meth.v17i4.6607.pdfAdobe PDF - 7.4MBMD5: b7bf507a0daaf5382d5f850e1e6a2e83
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There are no other versions of this object.
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Author(s) / Creator(s)Mason, Fabio
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Author(s) / Creator(s)Cantoni, Eva
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Author(s) / Creator(s)Ghisletta, Paolo
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PsychArchives acquisition timestamp2022-04-14T11:24:57Z
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Made available on2022-04-14T11:24:57Z
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Date of first publication2021-12-17
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Abstract / DescriptionThe linear mixed model (LMM) is a popular statistical model for the analysis of longitudinal data. However, the robust estimation of and inferential conclusions for the LMM in the presence of outliers (i.e., observations with very low probability of occurrence under Normality) is not part of mainstream longitudinal data analysis. In this work, we compared the coverage rates of confidence intervals (CIs) based on two bootstrap methods, applied to three robust estimation methods. We carried out a simulation experiment to compare CIs under three different conditions: data 1) without contamination, 2) contaminated by within-, or 3) between-participant outliers. Results showed that the semi-parametric bootstrap associated to the composite tau-estimator leads to valid inferential decisions with both uncontaminated and contaminated data. This being the most comprehensive study of CIs applied to robust estimators of the LMM, we provide fully commented R code for all methods applied to a popular example.en_US
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Publication statuspublishedVersion
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Review statuspeerReviewed
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CitationMason, F., Cantoni, E., & Ghisletta, P. (2021). Parametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed models. Methodology, 17(4), 271-295. https://doi.org/10.5964/meth.6607en_US
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ISSN1614-2241
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Persistent Identifierhttps://hdl.handle.net/20.500.12034/5710
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Persistent Identifierhttps://doi.org/10.23668/psycharchives.6314
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Language of contenteng
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PublisherPsychOpen GOLD
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Is version ofhttps://doi.org/10.5964/meth.6607
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Is related tohttps://doi.org/10.23668/psycharchives.5302
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Is related tohttps://github.com/masonFG/CIrobustLMM
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Is related tohttps://doi.org/10.23668/psycharchives.6322
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Keyword(s)robustnessen_US
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Keyword(s)linear mixed modelsen_US
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Keyword(s)bootstrapen_US
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Keyword(s)confidence intervalsen_US
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Keyword(s)longitudinal dataen_US
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Dewey Decimal Classification number(s)150
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TitleParametric and semi-parametric bootstrap-based confidence intervals for robust linear mixed modelsen_US
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DRO typearticle
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Issue4
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Journal titleMethodology
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Page numbers271–295
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Volume17
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Visible tag(s)Version of Recorden_US