Global ETD Search

11	Marginal modeling of longitudinal, binary response data : semiparametric and parametric estimation with long response series and an efficient outcome dependent sampling design / Schildcrout, Jonathan Scott, January 2004 (has links) Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 139-144).
12	Sieve analysis : statistical methods for assessing differential vaccine protection against human immunodeficiency virus types / Gilbert, Peter Brian. January 1996 (has links) Thesis (Ph. D.)--University of Washington, 1996. / Includes bibliographical references (leaves [213]-237).
13	Gaussian Process Multiclass Classification : Evaluation of Binarization Techniques and Likelihood Functions Ringdahl, Benjamin January 2019 (has links) In binary Gaussian process classification the prior class membership probabilities are obtained by transforming a Gaussian process to the unit interval, typically either with the logistic likelihood function or the cumulative Gaussian likelihood function. Multiclass classification problems can be handled by any binary classifier by means of so-called binarization techniques, which reduces the multiclass problem into a number of binary problems. Other than introducing the mathematics behind the theory and methods behind Gaussian process classification, we compare the binarization techniques one-against-all and one-against-one in the context of Gaussian process classification, and we also compare the performance of the logistic likelihood and the cumulative Gaussian likelihood. This is done by means of two experiments: one general experiment where the methods are tested on several publicly available datasets, and one more specific experiment where the methods are compared with respect to class imbalance and class overlap on several artificially generated datasets. The results indicate that there is no significant difference in the choices of binarization technique and likelihood function for typical datasets, although the one-against-one technique showed slightly more consistent performance. However the second experiment revealed some differences in how the methods react to varying degrees of class imbalance and class overlap. Most notably the logistic likelihood was a dominant factor and the one-against-one technique performed better than one-against-all. Machine learning Gaussian process classification Laplace's approximation Likelihood functions Binarization techniques Class imbalance Mathematics Matematik
14	Genetic association studies : exploiting SNP-haplotype selection and covariate independence / Dai, Yu. January 2007 (has links) Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 105-113).
15	Disequilibrium fine-mapping of a rare allele via coalescent models of gene ancestry / Graham, Jinko, January 1998 (has links) Thesis (Ph. D.)--University of Washington, 1998. / Vita. Includes bibliographical references (p. [119]-127).
16	Semiparametric marginal mean models for multivariate counting processes / Liu, Hao, January 2004 (has links) Thesis (Ph. D.)--University of Washington, 2004. / Vita. Includes bibliographical references (p. 364-373).
17	A comparison of three trough to peak estimators derived from ambulatory blood pressure data / Jones, Robert A. January 1997 (has links) Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [216]-217).
18	An evaluation of saddlepoint approximations in the generalized linear model / Platt, Robert William, January 1996 (has links) Thesis (Ph. D.)--University of Washington, 1996. / Vita. Includes bibliographical references (leaves [127]-133).
19	Comparing the performance of four calculation methods for estimating the sample size in repeated measures clinical trials where difference in treatment groups means is of interest Hagen, Clinton Ernest. January 2008 (has links) (PDF) Thesis--University of Oklahoma. / Bibliography: leaf 51.
20	A comparison of stratified and unstratified modeling for binary logistic regression in the presence of a simulated interaction Beebe, Claire Elizabeth. January 2008 (has links) (PDF) Thesis--University of Oklahoma. / Bibliography: leaves 48-49.

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