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Modelling covariance structure in bivariate marginal models for longitudinal data

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dc.contributor.author Xu, Jing
dc.contributor.author MacKenzie, Gilbert
dc.date.accessioned 2013-05-22T13:42:17Z
dc.date.available 2013-05-22T13:42:17Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10344/3113
dc.description peer-reviewed en_US
dc.description.abstract It can be more challenging and demanding to efficiently model the covariance matrices for multivariate longitudinal data than for univariate case because of the correlations between responses arising from multiple variables and repeated measurements over time. In addition to the more complicated covariance structures, the positive-definiteness constraint is still the major obstacle in modelling covariance matrices as in univariate case. In this paper, we develop a data-based method to model the covariance structures. Using this method, the constrained and hard-to-model parameters of ∑i are traded in for uncon- strained and interpretable parameters. Estimates of these parameters, together with the parameters in the mean, are obtained by maximum likelihood approach, and the large- sample asymptotic properties are derived when the observations are normally distributed. A simulation is carried out to illustrate the asymptotics. Application to a set of bivariate visual data shows that our method performs very well even when modelling bivariate nonstationary dependence structures. en_US
dc.language.iso eng en_US
dc.publisher Oxford University Press en_US
dc.relation.ispartofseries Biometrika;99(3), pp. 649-662
dc.relation.uri http:/dx.doi.org/10.1093/biomet/ass031
dc.rights This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version Modelling covariance structure in bivariate marginal models for longitudinal data, 2012,99(3), pp. 649-662 is available online at:http://dx.doi.org/10.1093/biomet/ass031 en_US
dc.subject longitudinal data en_US
dc.subject bivariate marginal models en_US
dc.subject covariance modelling en_US
dc.subject block triangular factorization en_US
dc.subject matrix logarithm en_US
dc.subject modelling log-innovation matrices en_US
dc.title Modelling covariance structure in bivariate marginal models for longitudinal data en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 07/MI/012 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 1399792


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