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.
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.
This thesis is to identify the underlying structures of multivariate time series and propose a methodology to construct predictive VAR models. Due to the complexity of high dimensions in multivariate time series, forecasting a target...
Statisticians often encounter data in the form of a combination of discrete and continuous outcomes. A special case is zero-inflated longitudinal data where the response variable has a large portion of zeros. These data exhibit...
As the rise in availability of natural language data, the underlying language structures can be better learned and play the important roles in many natural language processing tasks. Although the neural language representation models...
In recent years, researchers have become increasingly interested in deep learning. Extensive and high-quality data are essential for deep learning. For example, PubMed is a popular and free search engine with more than 30 million...
Since the scientific study of birdsong began in the late 1950s, songbirds have emerged as impressive neurobiological models for aspects of human verbal communication because they learn to sequence their song elements, analogous, in many...
This paper considers a few problems in the area of bioinformatics. First the problem of comparing distributions of chromosomal shapes estimated from wild type and gene knock-out Hi-C data. For each contact data matrix, we estimate...
An object of interest in an image can be characterized to some extent by the shape of its external boundary. Current techniques for shape analysis consider the notion of shape to be invariant to the similarity transformations (rotation, ...
The autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) models take the dependency of the conditional second moments. The idea behind ARCH/GARCH model is quite...
This dissertation presents, applies, and evaluates a statistical approach to an ocean circulation problem. The objective is to produce a map of ocean velocity in the North Atlantic based on sparse measurements along ship tracks, based on...
With the increasing popularity of information technology, especially electronic imaging techniques, large amount of high dimensional data such as 3D shapes become pervasive in science, engineering and even people's daily life, in the...
Shape analysis of curves and surfaces is a very important tool in many applications ranging from computer vision to bioinformatics and medical imaging. There are many difficulties when analyzing shapes of parameterized curves and...
Registration often plays a critical role in many fields, including but not limited to computer vision, shape analysis, and human activity recognition. Curves and times series data usually present a sort of misalignment, and it impedes a...
The prediction of financial time series is an essential topic in quantitative investment. In this dissertation, we proposed two types of new models. They are bidirectional encoder representations from Transformers-based financial...
Statistical analysis of functional data requires tools for comparing, summarizing and modeling observed functions as elements of a function space. A key issue in Functional Data Analysis (FDA) is the presence of the phase variability in...
Utilizing high throughput gene expression data stored in public archives not only saves research time and cost but also enhances the power of its statistical support. However, gene expression profiling data can be obtained from many...
Estimation of functions is an extremely rich and well-researched topic of research with broad applications spanning several scientific fields. We develop a shape based framework for probability density and general function modelling. The...
We propose a novel Riemannian framework for analyzing orientation distribution functions (ODFs), or their probability density functions (PDFs), in HARDI data sets for use in comparing, interpolating, averaging, and denoising PDFs. This...
Neuron morphology plays a central role in characterizing cognitive health and functionality of brain structures. The problem of quantifying neuron shapes, and capturing statistical variability of shapes, is difficult because axons and...
This research is motivated by an analysis of reading research data. We are interested in modeling the test outcome of ability to fluently recode letters into sounds of kindergarten children aged between 5 and 7. The data showed excessive...
Nonstationary nonparametric densities occur naturally including applications such as monitoring the amount of toxins in the air and in monitoring internet streaming data. Progress has been made in estimating these densities, but there is...
Functional variables serve important roles as predictors in a variety of pattern recognition and vision applications. Focusing on a specific subproblem, termed scalar-on-function regression, most current approaches adopt the standard L2...
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...
This thesis presents a stochastic process and time series study on corporate credit rating and market implied rating transitions. By extending an existing model, this paper incorporates the generalized autoregressive conditional...
This paper aims at analyzing a macro economy with a continuum of infinitely-lived households that make rational decisions about consumption and wealth savings in the face of employment and aggregate productivity shocks. The heterogeneous...
This dissertation includes mainly four chapters. The first chapter studies change-points in human brain functional connectivity (FC) and seek patterns that are common across multiple subjects under identical external stimulus. FC, ...
In this dissertation, we focus on the problem of analyzing high-dimensional functional data using geometric approaches. The term functional data refers to images, densities and trajectories on manifolds. The nature of these data imposes...
This research provides theoretical and computational developments in statistical shape analysis of shape graphs, and demonstrates them using analysis of complex network-type object data such as retinal blood-vessel (RBV) networks. The...
This dissertation studies statistical shape analysis of planar objects. The focus is on two different representations. The first one considers only the boundary of planar shapes, a comprehensive analysis framework including...
With the rapid development of Artificial Intelligence, Machine Learning technologies have entered a fast-evolving era. This dissertation covers application studies of various algorithms ranging from traditional machine learning methods...
With the advent of tremendous and complex data sets, the accessibility of data has become easier than before. Data integration thus became a popular method to deal with multi-source data. Identifying a dynamic association among multi...
Gene-expression data profile are widely used in all kinds of biomedical studies especially in cancer research. This dissertation work focus on solving the problem of how to combine datasets arising from different studies. Of particular...
Deep neural networks have drawn much attention due to their success in various vision tasks. Incremntal Leaning is a paradigm where instances from new object classes are added sequentially. The traditional training scheme causes a...
Statistical process control (SPC) is widely used in industrial settings to monitor processes for shifts in their distributions. SPC is generally thought of in two distinct phases: Phase I, in which historical data is analyzed in order to...
Large volumes of genomic data and new scientific discoveries in biomedical research are being made every day by laboratories in both academia and industry. However, two issues severely affect the usability of so-called biomedical big...
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...
My dissertation focuses on how the brain encodes a learned motor sequence. I look at the encoding of birdsong in the zebra finch (Taeniopygia guttata) to understand the mechanisms that underlie motor encoding. Juvenile zebra finches...
This dissertation contains two related parts. The first part focuses on affine lines, and the second part is about affine planes. In the first part, it develops tools for statistical analysis on spaces of affine lines. Motivated by the...
We are interested in differences between risk models based on Coronary Heart Disease (CHD) incidence, or morbidity, compared to risk models based on CHD death. Risk models based on morbidity have been developed based on the Framingham...
The stochastic modeling of financial assets is essential to the valuation of financial products and investment decisions. These models are governed by certain parameters that are estimated through a process known as calibration. Current...
We present two studies incorporating existing biological knowledge into differential gene expression analysis that attempt to place the results within a broader biological context. The studies investigate breast cancer health disparity...
Complex analyses involving multiple, dependent random quantities often lead to graphical models – a set of nodes denoting variables of interest, and corresponding edges denoting interactions between nodes. Such graphical data emerges in...
In this Dissertation, I report parallel processing in the avian vocal premotor nucleus HVC. First, I review historical ideas and recent evidence about the function of HVC. HVC has historically been viewed as a distributed network, and it...
The problem of comparisons of shape populations is present in many branches of science, including nano-manufacturing, medical imaging, particle analysis, fisheries, seed science, and computer vision. Researchers in these fields have...
Biomedial literature usually contains cutting edge research results on biomedical entities, in particular the relation between them, for example the interaction between two proteins. However, the ever-increasing volume in the literature...
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.
