Thesis (Ph. D.)--School of Electrical and Computer Engineering, Georgia Institute of Technology, 2004. Directed by Mark A. Clements. / Vita. Includes bibliographical references (leaves 176-184).

Thesis (Ph. D.)--University of Chicago, Department of Statistics, June 2001. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Washington, 2000. / Vita. Includes bibliographical references (p. 109-112).

Thesis (Ph. D.)--University of Chicago, Department of Statistics, August 2003. / Includes bibliographical references. Also available on the Internet.

Thesis (Ph. D.)--University of Kentucky, 2001. / Title from document title page. Document formatted into pages; contains viii, 89 p. : ill. Includes abstract. Includes bibliographical references (p. 81-86).

Thesis (Ph. D.)--York University, 1999. Graduate Programme in Mathematics and Statistics. / Typescript. Includes bibliographical references (leaves 125-127). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://wwwlib.umi.com/cr/yorku/fullcit?pNQ43460.

Thesis (Ph.D)--Industrial and Systems Engineering, Georgia Institute of Technology, 2010. / Committee Chair: Garrow, Laurie; Committee Co-Chair: Castillo, Marco; Committee Co-Chair: Goldsman, David; Committee Member: Griffin, Paul; Committee Member: White, Chelsea (Chip). Part of the SMARTech Electronic Thesis and Dissertation Collection.

van Norden and Schaller (1996) develop a standard regime-switching model to study stock market crashes. In their seminal paper, they use the maximum likelihood estimation to estimate the model parameters and show that a two-regime speculative bubble model has significant explanatory power for stock market returns in some observed periods. However, it is well known that the maximum likelihood estimation can lead to bias if the model contains multiple local maximum points or the estimation starts with poor initial values. Therefore, a better approach to estimate the parameters in the regime-switching models is to be found. One possible way is the Bayesian Gibbs-sampling approach, where its advantages are well discussed in Albert and Chib (1993). In this thesis, the Bayesian Gibbs-sampling estimation is examined by using two U.S. stock datasets: CRSP monthly value-weighted index from Jan 1926 to Dec 2010 and S&P 500 index from Jan 1871 to Dec 2010. It is found that the Gibbs-sampling estimation explains the U.S. data better than the maximum likelihood estimation. Moreover, the existing standard regime-switching speculative behaviour model is extended by considering the time-varying transition probabilities which are governed by the first-order Markov chain. It is shown that the time-varying first-order transition probabilities of Markov regime-switching speculative rational bubbles can lead stock market returns to have a second-order Markov regime. In addition, a Bayesian Gibbs-sampling algorithm is developed to estimate the parameters in the second-order two-state Markov regime-switching model. / published_or_final_version / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy

Many problems within the biological sciences, such as DNA sequencing, protein structure prediction, and molecular docking, are being approached computationally. These problems require sophisticated solution methods that understand the complex natures of biological domains. Traditionally, such solution methods are problem specific, but recent advances in generic problem-solvers furnish hope for a new breed of computational tools. The challenge is to develop methods that can automatically learn or acquire an understanding of a complex problem domain. Estimation of Distribution Algorithms (EDAs) are generic search methods that use statistical models to learn the structure of a problem domain. EDAs have been successfully applied to many difficult search problems, such as circuit design, optimizing Ising spin glasses, and various scheduling tasks. However, current EDAs contain ad hoc limitations that reduce their capacity to solve hard problems. This dissertation presents a new EDA method, the Markovian Learning Estimation of Distribution Algorithm (MARLEDA), that employs a Markov random field model. The model is learned in a novel way that overcomes previous ad hoc limitations. MARLEDA is shown to perform well on standard benchmark search tasks. A multiobjective extension of MARLEDA is developed for use in predicting the secondary structure of RNA molecules. The extension is shown to produce high-quality predictions in comparison with several contemporary methods, laying the groundwork for a new computational tool for RNA researchers.

published_or_final_version / Statistics / Doctoral / Doctor of Philosophy