A cautionary note on generalized linear models for covariance of unbalanced longitudinal data

Handle URI:
http://hdl.handle.net/10754/597228
Title:
A cautionary note on generalized linear models for covariance of unbalanced longitudinal data
Authors:
Huang, Jianhua Z.; Chen, Min; Maadooliat, Mehdi; Pourahmadi, Mohsen
Abstract:
Missing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations. © 2011 Elsevier B.V.
Citation:
Huang JZ, Chen M, Maadooliat M, Pourahmadi M (2012) A cautionary note on generalized linear models for covariance of unbalanced longitudinal data. Journal of Statistical Planning and Inference 142: 743–751. Available: http://dx.doi.org/10.1016/j.jspi.2011.09.011.
Publisher:
Elsevier BV
Journal:
Journal of Statistical Planning and Inference
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Mar-2012
DOI:
10.1016/j.jspi.2011.09.011
Type:
Article
ISSN:
0378-3758
Sponsors:
Huang and Pourahmadi were partially supported by NSF of the US. Huang was also supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorHuang, Jianhua Z.en
dc.contributor.authorChen, Minen
dc.contributor.authorMaadooliat, Mehdien
dc.contributor.authorPourahmadi, Mohsenen
dc.date.accessioned2016-02-25T12:28:26Zen
dc.date.available2016-02-25T12:28:26Zen
dc.date.issued2012-03en
dc.identifier.citationHuang JZ, Chen M, Maadooliat M, Pourahmadi M (2012) A cautionary note on generalized linear models for covariance of unbalanced longitudinal data. Journal of Statistical Planning and Inference 142: 743–751. Available: http://dx.doi.org/10.1016/j.jspi.2011.09.011.en
dc.identifier.issn0378-3758en
dc.identifier.doi10.1016/j.jspi.2011.09.011en
dc.identifier.urihttp://hdl.handle.net/10754/597228en
dc.description.abstractMissing data in longitudinal studies can create enormous challenges in data analysis when coupled with the positive-definiteness constraint on a covariance matrix. For complete balanced data, the Cholesky decomposition of a covariance matrix makes it possible to remove the positive-definiteness constraint and use a generalized linear model setup to jointly model the mean and covariance using covariates (Pourahmadi, 2000). However, this approach may not be directly applicable when the longitudinal data are unbalanced, as coherent regression models for the dependence across all times and subjects may not exist. Within the existing generalized linear model framework, we show how to overcome this and other challenges by embedding the covariance matrix of the observed data for each subject in a larger covariance matrix and employing the familiar EM algorithm to compute the maximum likelihood estimates of the parameters and their standard errors. We illustrate and assess the methodology using real data sets and simulations. © 2011 Elsevier B.V.en
dc.description.sponsorshipHuang and Pourahmadi were partially supported by NSF of the US. Huang was also supported by Award Number KUS-CI-016-04, made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherElsevier BVen
dc.subjectCholesky decompositionen
dc.subjectJoint mean-covariance modelingen
dc.subjectMissing dataen
dc.titleA cautionary note on generalized linear models for covariance of unbalanced longitudinal dataen
dc.typeArticleen
dc.identifier.journalJournal of Statistical Planning and Inferenceen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionExxonMobil, Irving, United Statesen
kaust.grant.numberKUS-CI-016-04en
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