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dc.contributor.supervisor Torabi, Mahmoud (Statistics) en_US
dc.contributor.author Hoque, Md. Erfanul
dc.date.accessioned 2017-01-05T19:36:01Z
dc.date.available 2017-01-05T19:36:01Z
dc.date.issued 2016
dc.identifier.uri http://hdl.handle.net/1993/31988
dc.description.abstract Longitudinal data occur frequently in medical studies and covariates measured by error are typical features of such data. Generalized linear mixed models (GLMMs) are commonly used to analyse longitudinal data. It is typically assumed that the random effects covariance matrix is constant across the subject (and among subjects) in these models. In many situations, however, this correlation structure may differ among subjects and ignoring this heterogeneity can cause the biased estimates of model parameters. In this thesis, following Lee et al. (2012), we propose an approach to properly model the random effects covariance matrix based on covariates in the class of GLMMs where we also have covariates measured by error. The resulting parameters from this decomposition have a sensible interpretation and can easily be modelled without the concern of positive definiteness of the resulting estimator. The performance of the proposed approach is evaluated through simulation studies which show that the proposed method performs very well in terms biases and mean square errors as well as coverage rates. The proposed method is also analysed using a data from Manitoba Follow-up Study. en_US
dc.subject Cholesky decomposition en_US
dc.subject Longitudinal data en_US
dc.subject Measurement error en_US
dc.subject Monte Carlo Expectation-maximization algorithm en_US
dc.subject Random effects en_US
dc.subject Generalized Linear Mixed Model en_US
dc.title Longitudinal data analysis with covariates measurement error en_US
dc.degree.discipline Statistics en_US
dc.contributor.examiningcommittee Wang, Liqun (Statistics) Tate, Robert B. (Community Health Sciences) en_US
dc.degree.level Master of Science (M.Sc.) en_US
dc.description.note February 2017 en_US


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