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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.
In meta-analysis practice, effect measures from individual studies are synthesized to produce an overall result. Researchers frequently face studies that report the same outcome differently. The first scenario is that continuous outcomes...
In the rapidly evolving world of molecular biology, genetics and epigenetics have taken crucial roles in unraveling the complex origins of diseases. Through gene expression, genes govern the synthesis of proteins, the fundamental...
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...
The pair correlation function is very commonly used in describing the degree of attraction or repulsion between points in stationary point processes. Non-stationary spatial point processes arise in many applications, and it would be...
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...
The high mortality rate and huge expenditure caused by dementia makes it a pressing concern for public health researchers. Among the potential risk factors in diet and nutrition, the relation between alcohol usage and dementia has been...
Multi-state models are models for a process, which at any time occupies one of several possible states. An example of a multi-state process is the life history of an individual, where the states can be different diseases and an absorbing...
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...
Genes with moderate to low expression heritability may explain a large proportion of complex trait etiology, but such genes cannot be sufficiently captured in conventional transcriptome-wide association studies (TWASs), partly due to the...
Protecting individuals' private information while still allowing modelers to draw inferences from confidential data sets is a concern of many data producers. Differential privacy is a framework that enables statistical analyses while...
Modern statistical problems often involve minimizing objective functions that are not necessarily convex or smooth. In this study, we devote to developing scalable algorithms for nonconvex optimization with statistical guarantees. We...
Big-data applications typically involve huge numbers of observations and features, thus posing new challenges for variable selection and parameter estimation. In addition to being efficient, the desired algorithm is better to have...
The genetic architecture of Alzheimer's disease is largely unknown. Imaging-wide association study (IWAS) that integrates brain imaging information with genome-wide association studies (GWAS) results have successfully enhanced the...
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...
The study of point processes and the analysis of data which involves spatial point patterns are important topics in spatial statistics with a long history and a large literature. A relatively new sub-area within this is the statistical...
Volatility is usually employed to measure the dispersion of asset returns, and it’s widely used in risk analysis and asset management. This first chapter studies a kernel-based spot volatility matrix estimator with pre-averaging approach...
Goodness-of-fit tests are important to assess how well a model fits a set of observations. Hosmer-Lemeshow (HL) test is a popular and commonly used method to assess the goodness-of-fit for logistic regression. However, there are two...
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.
