RJMCMC algorithm for multivariate Gaussian mixtures with applications in linear mixed-effects models /Ho, Kwok Wah. January 2005 (has links)
Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 77-82). Also available in electronic version.
A comparison of estimators in hierarchical linear modeling restricted maximum likelihood versus bootstrap via minimum norm quadratic unbiased estimators /Delpish, Ayesha Nneka. Niu, Xu-Feng. January 2006 (has links)
Thesis (Ph. D.)--Florida State University, 2006. / Advisor: Xu-Feng Niu, Florida State University, College of Arts and Sciences, Dept. of Statistics. Title and description from dissertation home page (viewed Sept. 18, 2006). Document formatted into pages; contains ix, 116 pages. Includes bibliographical references.
Thesis (Ph. D.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.
Rausch, Joseph R.
Thesis (Ph. D.)--University of Notre Dame, 2006. / Thesis directed by Scott E. Maxwell and Steven M. Boker for the Department of Psychology. "July 2006." Includes bibliographical references (leaves 105-119).
An approach to estimating the variance components to unbalanced cluster sampled survey data and simulated dataRamroop, Shaun 30 November 2002 (has links)
Statistics / M. Sc. (Statistics)
16 September 2015
M.Sc. / Please refer to full text to view abstract
McMillan, David Evans
17 November 2012
There is a vast amount of research being done in the area of voice recognition. A large portion of this research concentrates on developing algorithms that will yield higher accuracy rates; such as algorithms based on dynamic time warping, vector quantization, and other mathematical methods [l2][l5]. In this research, the evaluation of the feasibility of using linear prediction (LP) with time-varying parameters as a base for a voice recognition algorithm will be investigated. First the development of an anti-aliasing filter is discussed with some results from the filter hardware realization included. Then a brief discussion of LP is presented and a method for time-varying LP is derived from this discussion. A comparison between time-varying and segmentation LP is made and a description of the developed algorithm that tests time-varying LP as a recognition technique is given. The evaluation is conducted with the developed algorithm configured for speaker-dependent and speaker-independent isolated-word recognition. The conclusion drawn from this research is that this particular technique of voice recognition is very feasible as a base for a voice recognition algorithm. With the incorporation of other techniques, a complete algorithm can conceivably be developed that will yield very high accuracy rates. Recommendations for algorithm improvements are given along with other techniques that might be added to make a complete recognition algorithm. / Master of Science
Ill-conditioned information matrices and the generalized linear model: an asymptotically biased estimation approachMarx, Brian D. January 1988 (has links)
In the regression framework of the generalized linear model (Nelder and Wedderburn (1972)), interative maximum likelihood parameter estimation is employed via the method of scoring. This iterative procedure involves a key matrix, the information matrix. Ill-conditioning of the information matrix can be responsible for making many desirable properties of the parameter estimates unattainable. Some asymptotically biased alternatives to maximum likelihood estimation are put forth which alleviate the detrimental effects of near singular information. Notions of ridge estimation (Hoerl and Kennard (1970a) and Schaefer (1979)), principal component estimation (Webster et al. (1974) and Schaefer (1986)), and Stein estimation (Stein (1960)) are extended into a regression setting utilizing any one of an entire class of response distributions. / Ph. D.
DeFeo, Patrick A.
General Response Surface Methodology involves the exploration of some response variable which is a function of other controllable variables. Many criteria exist for selecting an experimental design for the controllable variables. A good choice of a design is one that may not be optimal in a single sense, but rather near optimal with respect to several criteria. This robust approach can lend well to strategies that involve sequential or two stage experimental designs. An experimenter that fits a first order regression model for the response often fears the presence of curvature in the system. Experimental designs can be chosen such that the experimenter who fits a first order model will have a high degree of protection against potential model bias from the presence of curvature. In addition, designs can also be selected such that the experimenter will have a high chance for detection of curvature in the system. A lack of fit test is usually performed for detection of curvature in the system. Ideally, an experimenter desires good detection capabilities along with good protection capabilities. An experimental design criterion that incorporates both detection and protection capabilities is the A₂* criterion. This criterion is used to select the designs which maximize the average noncentrality parameter of the lack of fit test among designs with a fixed bias. The first order rotated design class is a new class of designs that offers an improvement in terms of the A₂* criterion over standard first order factorial designs. In conjunction with a sequential experimental strategy, a class of second order rotated designs are easily constructed by augmenting the first order rotated designs. These designs allow for estimation of second order model terms when a significant lack of fit is observed. Two other design criteria, that are closely related, and incorporate both detection and protection capabilities are the J<sub>PCA</sub>, and J<sub>PCMAX</sub> criterion. J<sub>PCA</sub>, considers the average mean squared error of prediction for a first order model over a region where the detection capabilities of the lack of fit test are not strong. J<sub>PCMAX</sub> considers the maximum mean squared error of prediction over the region where the detection capabilities are not strong. The J<sub>PCA</sub> and J<sub>PCMAX</sub> criteria are used within a sequential strategy to select first order experimental designs that perform well in terms of the mean squared error of prediction when it is likely that a first order model will be employed. These two criteria are also adopted for nonsequential experiments for the evaluation of first order model prediction performance. For these nonsequential experiments, second order designs are used and constructed based upon J<sub>PCA</sub> and J<sub>PCMAX</sub> for first order model properties and D₂ -efficiency and D-efficiency for second order model properties. / Ph. D.
Jin, Shusong., 金曙松.
published_or_final_version / abstract / Statistics and Actuarial Science / Doctoral / Doctor of Philosophy
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