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An investigation of the type I error rates and power of standard and alternative multivariate tests on means under homogeneous and heterogeneous covariance matrices and multivariate normality and nonnormality /Yockey, Ron David, January 2000 (has links)
Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 316-324). Available also in a digital version from Dissertation Abstracts.
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Analysis of zero-inflated count dataWan, Chung-him. January 2009 (has links)
Thesis (M. Phil.)--University of Hong Kong, 2010. / Includes bibliographical references (leaves 100-104). Also available in print.
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Variance-based clustering methods and higher order data transformations and their applicationsLytkin, Nikita I. January 2009 (has links)
Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Computer Science." Includes bibliographical references (p. 78-82).
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Selecting the best process variables for classification of production batches into quality levelsAnzanello, Michel Jose, January 2009 (has links)
Thesis (Ph. D.)--Rutgers University, 2009. / "Graduate Program in Industrial and Systems Engineering." Includes bibliographical references (p. 78-84).
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Sample size when the alternative is ordered and other multivariate resultsMcIntosh, Matthew J. January 1998 (has links)
Thesis (Ph. D.)--University of Missouri-Columbia, 1998. / Typescript. Vita. Includes bibliographical references (leaves 244-246). Also available on the Internet.
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Strategies for chemometric analysis of gas chromatographic data /Johnson, Kevin J., January 2003 (has links)
Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 145-155).
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Adaptive prefetching for visual data explorationDoshi, Punit Rameshchandra. January 2003 (has links)
Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: Adaptive prefetching; Large-scale multivariate data visualization; Semantic caching; Hierarchical data exploration; Exploratory data analysis. Includes bibliographical references (p.66-70).
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The theory and application of multivariate and conditional definitions of normality in clinical medicine /Fung, Shing-chung. January 1984 (has links)
Thesis--M. Phil., University of Hong Kong, 1986.
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Statistical inference of a threshold model in extreme value analysisLee, David., 李大為. January 2012 (has links)
In many data sets, a mixture distribution formulation applies when it is
known that each observation comes from one of the underlying categories. Even
if there are no apparent categories, an implicit categorical structure may justify
a mixture distribution. This thesis concerns the modeling of extreme values in
such a setting within the peaks-over-threshold (POT) approach. Specifically,
the traditional POT modeling using the generalized Pareto distribution is augmented
in the sense that, in addition to threshold exceedances, data below the
threshold are also modeled by means of the mixture exponential distribution.
In the first part of this thesis, the conventional frequentist approach is
applied for data modeling. In view of the mixture nature of the problem,
the EM algorithm is employed for parameter estimation, where closed-form
expressions for the iterates are obtained. A simulation study is conducted to
confirm the suitability of such method, and the observation of an increase in
standard error due to the variability of the threshold is addressed. The model
is applied to two real data sets, and it is demonstrated how computation time
can be reduced through a multi-level modeling procedure. With the fitted
density, it is possible to derive many useful quantities such as return periods
and levels, value-at-risk, expected tail loss and bounds for ruin probabilities.
A likelihood ratio test is then used to justify model choice against the simpler
model where the thin-tailed distribution is homogeneous exponential.
The second part of the thesis deals with a fully Bayesian approach to the
same model. It starts with the application of the Bayesian idea to a special
case of the model where a closed-form posterior density is computed for the
threshold parameter, which serves as an introduction. This is extended to
the threshold mixture model by the use of the Metropolis-Hastings algorithm
to simulate samples from a posterior distribution known up to a normalizing
constant. The concept of depth functions is proposed in multidimensional
inference, where a natural ordering does not exist. Such methods are then
applied to real data sets. Finally, the issue of model choice is considered
through the use of posterior Bayes factor, a criterion that stems from the
posterior density. / published_or_final_version / Statistics and Actuarial Science / Master / Master of Philosophy
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A polytomous nonlinear mixed model for item analysisShin, Seon-hi 25 July 2011 (has links)
Not available / text
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