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- Genre: Academic theses
- Genre: Masters Thesis
Description
Rapid advance in sensor and information technology has resulted in both spatially and temporally data-rich environment, which creates a pressing need for us to develop novel statistical methods and the associated computational tools to extract intelligent knowledge and informative patterns from these massive datasets. The statistical challenges for addressing these massive datasets lay in their complex structures, such as high-dimensionality, hierarchy, multi-modality, heterogeneity and data uncertainty. Besides the statistical challenges, the associated computational approaches are also considered essential in achieving efficiency, effectiveness, as well as the numerical stability in practice. On the other hand, some recent developments in statistics and machine learning, such as sparse learning, transfer learning, and some traditional methodologies which still hold potential, such as multi-level models, all shed lights on addressing these complex datasets in a statistically powerful and computationally efficient way. In this dissertation, we identify four kinds of general complex datasets, including "high-dimensional datasets", "hierarchically-structured datasets", "multimodality datasets" and "data uncertainties", which are ubiquitous in many domains, such as biology, medicine, neuroscience, health care delivery, manufacturing, etc. We depict the development of novel statistical models to analyze complex datasets which fall under these four categories, and we show how these models can be applied to some real-world applications, such as Alzheimer's disease research, nursing care process, and manufacturing.
ContributorsHuang, Shuai (Author) / Li, Jing (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012
Description
There is conflicting evidence regarding whether a biasing effect of neuroscientific evidence exists. Early research warned of such bias, but more recent papers dispute such claims, with some suggesting a bias only occurs in situations of relative judgment, but not in situations of absolute judgment. The current studies examined the neuroimage bias within both criminal and civil court case contexts, specifically exploring if a bias is dependent on the context in which the neuroimage evidence is presented (i.e. a single expert vs. opposing experts). In the first experiment 408 participants read a criminal court case summary in which either one expert witness testified (absolute judgment) or two experts testified (relative judgment). The experts presented neurological evidence in the form of functional magnetic resonance imaging (fMRI) data and the evidence type varied between a brain image and a graph. A neuroimage bias was found, in that jurors who were exposed to two experts were more punitive when the prosecution presented the image and less punitive when the defense did. In the second experiment 240 participants read a summary of a civil court case in which either a single expert witness testified or two experts testified. The experts presented fMRI data to support or refute a claim of chronic pain and the evidence type again varied between image and graph. The expected neuroimage bias was not found, in that jurors were more likely to find in favor of the plaintiff when either side proffered the image, but more likely to find for the defense when only graphs were offered by the experts. These findings suggest that the introduction of neuroimages as evidence may affect jurors punitiveness in criminal cases, as well as liability decisions in civil cases and overall serves to illustrate that the influence of neuroscientific information on legal decision makers is more complex than originally thought.
ContributorsHafdahl, Riquel J (Author) / Schweitzer, Nicholas (Thesis advisor) / Salerno, Jessica (Committee member) / Neal, Tess (Committee member) / Arizona State University (Publisher)
Created2016