Joint generalized nonlinear mixed models for longitudinal data

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dc.contributor.advisorHasan, Tariq
dc.contributor.advisorRenjun Ma
dc.contributor.authorHaque, Md Ashiqul
dc.date.accessioned2023-03-01T16:33:51Z
dc.date.available2023-03-01T16:33:51Z
dc.date.issued2020
dc.date.updated2023-03-01T15:02:41Z
dc.description.abstractJoint modeling of multiple longitudinal responses enables us to account for the association between them and is thus more efficient than seperate analyses. Most existing techniques to handle this problem are based on the assumptions of normality of the responses and linearity of the mean functions. However, non-normality of responses and non-linear shape of their mean functions often arise from medical and population growth studies. For example, it is desirable to investigate the nonlinear mean structures in the analysis of the effect of different drug formulations while accounting for their association in Pharmaco-dynamics (the study of what the drug does to the body). We propose to model data of mixed types jointly by incorporating both subject-specific and time-specific random effects into Tweedie nonlinear models. An optimal estimation procedure for our model has been developed using the orthodox best linear unbiased predictors of the random effects. This approach allows us to model multiple non-normal longitudinal responses with interpretable parameters.
dc.description.copyright© Ashiqul Haque, 2020
dc.formattext/xml
dc.format.extentix, 73 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/14113
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineMathematics and Statistics
dc.titleJoint generalized nonlinear mixed models for longitudinal data
dc.typemaster thesis
thesis.degree.disciplineMathematics and Statistics
thesis.degree.fullnameMaster of Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.Sc.

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