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Some of the material in is restricted to members of the community. By logging in, you may be able to gain additional access to certain collections or items. If you have questions about access or logging in, please use the form on the Contact Page.
Motivated by understanding the devastating financial crisis in 2008 that was partially caused by underestimation of financial risk, we propose a class of time-varying mixture models for risk analysis and management. There are various...
Longitudinal studies are widely used in various fields, such as public health, clinic trials and financial data analysis. A major challenge for longitudinal studies is repeated measurements from each subject, which cause time dependent...
Forecasting a univariate target time series in high dimensions with very many predictors poses challenges in statistical learning and modeling. First, many nuisance time series exist and need to be removed. Second, from economic theories...
Work is presented from two projects, each involving an application of machine learning to precision medicine. The first project was for the Document Triage Task of the BioCreative VI Precision Medicine Track. Teams were asked to build...
Nonparametric statistical methods can refer a wide variety of techniques. In this dissertation, we focus on two problems in statistics which are common applications of nonparametric statistics. The main body of the dissertation focuses...
In the field of functional data analysis, registration is still a fundamental problem. Registration still has to take into consideration what underlying template is chosen for "center" or alignment purposes and the hurdles that come with...
Shape analysis is a widely studied topic in modern Statistics with important applications in areas such as medical imaging. Here we focus on two-sample hypothesis testing for both finite and infinite extrinsic mean shapes of...
In this dissertation, we develop tools from non-parametric and semi-parametric statistics to perform estimation and inference. In the first chapter, we propose a new method called Non-Parametric Outlier Identification and Smoothing (NOIS...
Gaussian processes are not uncommon in various fields of science such as engineering, genomics, quantitative finance and astronomy, to name a few. In fact, such processes are special cases in a broader class of data known as functional...
To date, Object Data Analysis is the most inclusive type of data analysis, as far as the object spaces are concerned. It extends multivariate data analysis, landmark based shape analysis, and in the infinite dimensional case, it extends...
This dissertation defense is concerned with random objects in the complex projective space . It is shown that the Veronese-Whitney (VW) antimean, which is the extrinsic antimean of a random point on complex projective space relative to...
Count data are ubiquitous in modern statistical applications. How to modeling such data remains a challenging task in machine learning. In this study, we consider various aspects of statistical modeling on Poisson count data. Concerned...
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