Thesis (M. S.)--Mechanical Engineering, Georgia Institute of Technology, 2007. / Rahnema, Farzad, Committee Chair ; Fox, Tim, Committee Co-Chair ; Wang, Chris, Committee Member.

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xiv, 122 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 107-116).

Thesis (M.Sc.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on July 29, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Master of Science, Department of Computing Science, University of Alberta. Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 75-76). Also available in electronic version.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2005. / Includes bibliographical references (p. 65-69).

Thesis (Ph. D.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2007. / Includes bibliographical references (p. 53-55).

Dissertation (Ph. D.)--University of Akron, Dept. of Polymer Science, 2006. / "May, 2006." Title from electronic dissertation title page (viewed 10/11/2006) Advisor, Wayne L. Mattice; Committee members, Ernst D. von Meerwall, Ali Dhinojwala, Gustavo A. Carri, Richard J. Elliott; Department Chair, Mark D. Foster; Dean of the College, Frank N. Kelley; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

In this thesis, we investigate joint models of longitudinal and time-to-event data. We extend the current literature by developing a model that assigns subject-specific variance to the longitudinal process and links this variance to the survival outcome. During development we provide the theoretical definition of the model and its properties, and explore the practical implications for estimating the parameters. We use Markov Chain Monte Carlo (MCMC) methods, and compare the different samplers used in similar models in the literature with our custom MCMC algorithm, written in C++. We use the Deviance Information Criterion to perform model comparisons, and we formalise suggestions from the literature to use posterior predictive model checking to construct a goodness-of-fit test for our model. We use the model on two real-world datasets to investigate claims relating to the importance of blood pressure volatility on stroke risk, and examine the consequences of ignoring measurement error. We amend our model to accommodate competing risk, time-dependent baseline hazard rates, and bivariate longitudinal processes - at which point we update our MCMC samplers and identify the issues. Finally, we use our code in a separate, but related, collaboration with other researchers to analyse repeated counts data.