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No description available.

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Broadly speaking, cluster analysis is the organization of a data set into meaningful groups and mixture model-based clustering is recently receiving a wide interest in statistics. Historically, the Gaussian mixture model has dominated the model-based clustering literature. When model-based clustering is performed on a large number of observed variables, it is well known that Gaussian mixture models can represent an over-parameterized solution. To this end, this thesis focuses on the development of novel non-Gaussian mixture models for high-dimensional continuous and categorical data. We developed a mixture of joint generalized hyperbolic models (JGHM), which exhibits different marginal amounts of tail-weight. Moreover, it takes into account the cluster specific subspace and, therefore, limits the number of parameters to estimate. This is a novel approach, which is applicable to high, and potentially very- high, dimensional spaces and with arbitrary correlation between dimensions. Three different mixture models are developed using forms of the mixture of latent trait models to realize model-based clustering of high-dimensional binary data. A family of mixture of latent trait models with common slope parameters are developed to reduce the number of parameters to be estimated. This approach facilitates a low-dimensional visual representation of the clusters. We further developed the penalized latent trait models to facilitate ultra high dimensional binary data which performs automatic variable selection as well. For all models and families of models developed in this thesis, the algorithms used for model-fitting and parameter estimation are presented. Real and simulated data sets are used to assess the clustering ability of the models. / Thesis / Doctor of Philosophy (PhD)

Currently most batch processes run in an open loop manner with respect to final product quality, regardless of the performance obtained. This fact, allied with the increased industrial importance of batch processes, indicates that there is a pressing need for the development and dissemination of automated batch quality control techniques that suit present industrial needs. Within this context, the main objective of the current work is to exemplify the use of empirical latent variable methods to reduce product quality variability in batch processes. These methods are also known as multiway principal component analysis (MPCA) and partial least squares (MPLS) and were originally introduced by Nomikos and MacGregor (1992, 1994, 1995a and 1995b ). Their use is tied with the concepts of statistical process control (SPC) and lead to incremental process improvements. Throughout this thesis three different sets of industrial sets of data, originating from different batch process were analyzed. The first section of this thesis (Chapter 3) demonstrates how MPCA and multi-block, multiway, partial least squares (MB-MPLS) methods can be successfully used to troubleshoot an industrial batch unit in order to identify optimal process conditions with respect to quality. Additionally, approaches to batch data laundering are proposed. The second section (Chapter 4) elaborates on the use of a MPCA model to build a single, all-encompassing, on-line monitoring scheme for the heating phase of a multi-grade batch annealing process. Additionally, this same data set is used to present a simple alignment technique for batch data when on-line monitoring is intended (Chapter 5). This technique is referred to as pre-alignment and it relies on the use of a PLS model to predict the duration of new batches. Also, various methods for dealing with matrices containing different sized observations are proposed and evaluated. Finally, the last section (Chapter 6) deals with end-point prediction of a condensation polymerization process. / Thesis / Master of Applied Science (MASc)

Latent class modelling is one method used in the evaluation of diagnostic tests when there is no gold standard test that is perfectly accurate. The technique demonstrates maximum likelihood estimates of the prevalence of a disease or a condition and the error rates of diagnostic tests or observers. This study reports the effect of departures from the latent class model assumption of independent misclassifications between observers or tests conditional on the true state of the individual being tested. It is found that estimates become biased in the presence of dependence. Most commonly the prevalence of the disease is overestimated when the true prevalence is at less than 50% and the error rates of dependent observers are underestimated. If there are also independent observers in the group, their error rates are overestimated. The most dangerous scenario in which to use latent class methods int he evaluation of tests is when the true prevalence is low and the false positive rate is high. This is common to many screening situations. / Thesis / Master of Science (MS)

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Intimate partner violence (IPV) is a prevalent potentially traumatic experience that increases risk for posttraumatic stress symptoms (PTSS). However, there is still considerable heterogeneity in PTSS among women exposed to IPV. Research on IPV has examined the ways in which different characteristics of IPV exposure have separately related to risk for PTSS, specifically the type (physical, psychological, economic, sexual), frequency (number of incidents), severity (minor, severe), and mode of violence (in-person, online). However, it may be important to examine how the integration of these characteristics of IPV differ across ���������������������� ���� ������ ���� ������������ �������������������� �������������� ���������� The current study integrated these characteristics to assess classes of IPV and the relevant associations between concurrent and future PTSS. 264 women between the ages of 18-24 (Mage=20.41, SD=2.99) were recruited as part of a greater longitudinal study examining the relationship between PTSS and co-occurring psychopathology following exposure to IPV and/or sexual assault in the past year. Four classes of IPV across four characteristics of IPV (type, severity, frequency, and mode) were identified with latent class analysis (LCA). (1) history of both mild and severe psychological, physical, and sexual IPV in person and online, (2) history of mild and severe psychological IPV and mild sexual IPV occurring in person and online, (3) history of mild psychological IPV occurring in person and online, (4) past history of one type of IPV occurring in person. Class membership and concurrent and future PTSS were found to be associated with class membership.

Mercury (Hg) is an important environmental contaminant due to its global distribution, tendency to bioaccumulate, and toxicity to wildlife. However, Hg has received little attention in amphibians compared to other vertebrates, despite the fact that amphibian population declines have been documented worldwide and environmental contaminants are believed to contribute to some declines. During my dissertation research, I used a pluralistic approach which combined field studies and manipulative laboratory and mesocosm experiments to examine the bioaccumulation and ecological effects of environmentally relevant Hg exposure routes acting at various early life stages in amphibians. By collecting amphibians in the field at the Hg-contaminated South River, VA, I confirmed that amphibians exhibiting different life histories and occupying different ecological niches (Plethodon cinereus, Eurycea bislineata, and Bufo americanus) can bioaccumulate sufficient levels of Hg to warrant concern (Chapter 2) and female Bufo americanus transfer accumulated Hg to their eggs (Chapter 3). Maternal transfer of contaminants is a parental effect which typically has negative consequences for offspring because early development is a critical organizational period in the ontogeny of vertebrates. Through laboratory observations and mesocosm experiments, I examined the short and long-term effects of maternal contaminant exposure on offspring, and found the negative effects of maternal Hg exposure manifested either immediately at the embryonic stage or later during the larval stage, depending on the year in which the study was conducted (Chapters 4 and 5). Lastly, using a factorial laboratory experiment, I examined whether the latent effects of maternal transfer of contaminants manifests differently depending on the environment in which offspring develop, and found both maternal and dietary Hg exposure independently produced negative, but different, sublethal effects on larval development. Most importantly, maternal exposure to Hg combined with high dietary Hg exposure later in ontogeny had a lethal effect in larvae (Chapter 6). This study is one of the first to demonstrate that the latent effects of maternally transferred contaminants may be exacerbated by further exposure later in ontogeny, findings that may have important implications for both wildlife and human health. / Ph. D.