We consider a class of stationary processes that exhibit both long-range dependence and heavy tails. While separate limit theorems for the partial sums and the maxima of [...]

Accurate prediction of crop phenotypes is critical for breeding and agricul-tural sustainability but challenged by complex interactions between genetics, environment, and[...]

Genomic selection is a powerful tool for accelerating genetic gain in plant breeding by leveraging genome-wide markers. In this study, we evaluated genomic prediction acc[...]

This dissertation addresses two fundamental areas in statistics: the design of optimal physical experiments and the development of surrogate models for complex computer e[...]

Reconstructing transmission networks is critical for identifying epidemiological factors such as superspreaders and high-risk locations, informing targeted strategies for[...]

In this dissertation, we explore the problems of high-dimensional feature screening and sampling techniques using Quantum Walk. In the first project, we introduce a novel[...]

This thesis, conducted as part of the Active Learning Change Grant, examines the effectiveness of active learning techniques incorporated in large lecture-format classroo[...]

The rapid growth of complex, high-dimensional data—such as graphs and time series—has created a pressing need for robust and scalable representation learning methods. Geo[...]

The decreasing trend of cancer mortality has been mostly due to the improved diagnostic techniques for detecting the early stage of cancer as well as development of thera[...]

The spatial autocorrelation of crop yield violates the independence assumption of conventional methods such as ordinary least square (OLS). Therefore, in the model of cro[...]

This study examines the integration of multivariate extreme analysis within clustering techniques, specifically focusing on spherical k-means clustering and spherical k-p[...]

Schizophrenia is a severe mental disorder that affects millions of people and is subject to much research. Functional magnetic resonance imaging (fMRI) is a tool that pro[...]

This study investigated the performance of Actual Production History (APH), a farm level crop insurance plan, vis-a-vis Group Risk Plan (GRP), an area level crop insuranc[...]

The Generalized Extreme Value (GEV) models relevance to the extremes of a distribution, and the Generalized Pareto (GP) models relevance to the exceedences above a thresh[...]

Empirical likelihood is a nonparametric method based on a data-driven likelihood. The flexibility of empirical likelihood facilitates its use in complex settings, which c[...]

Extreme value theory is a branch of statistics that is devoted to studying the phenomena governed by extremely rare events. The modeling of such phenomena are tail depend[...]

In this thesis, we examine the properties that make the expectile a proper candidate for a measure of risk in finance. Expectiles are the solutions to asymmetric least sq[...]

Inferences from phylogenetic trees is useful in forensic science, bioinformatics, identifying pathogens, and other applications. Thus, building accurate trees is importa[...]

Assessments play an important role within any subject as a method to determine how well students are learning. Until recently within the field of statistics education the[...]

The availability of complete genomic sequences and microarray expression data calls for computational methods for characterizing transcriptome, the complete collection of[...]

This dissertation develops a number of nonparametric inference procedures for time to event data under right censoring. The type of event time data that we consider are b[...]

Risk management has become one of crucial parts of the daily operation in the financial industry. After the risk has been successfully quantified, the study of risk and r[...]

The likelihood function plays an essential role in statistical analysis. It helps to estimate a set of parameters of interest. To make inferences, usually one must specif[...]

The analysis of functional neuroimaging data often involves the simultaneous testing for activation at thousands of voxels, leading to a massive multiple testing problem.[...]

In this dissertation, two different areas of statistical genetics are explored. The first is a analysis of genotyping errors in microsatellite data and their effect on p[...]