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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.
Genome-wide association studies (GWAS) have significantly contributed to the identification of genetic variants by leveraging thousands of loci associated with complex traits and diseases, leading to breakthroughs in human genetics...
In some large clinical studies, it may be impractical to give physical examinations to every subject at his/her last monitoring time in order to diagnose the occurrence of an event of interest. This challenge creates survival data with...
The age of big data has re-invited much interest in dimension reduction. How to cope with high-dimensional data remains a difficult problem in statistical learning. In this study, we consider the task of dimension reduction---projecting...
Spatial datasets are becoming increasingly more common over the recent decades. Rapid devel- opments in technology has brought an abundance of information and data. Big spatial datasets produce many computational challenges. In this...
Data skewness widely occurs in present-day statistical applications. Various distribution families have been recently proposed to model skewed data, many enforcing different scales in reference to the median or the mode. We argue that...
The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics--means, proportions, totals, etcetera. Using a model-based approach, complex...
This dissertation is concerned with high level imaging analysis. In particular, our focus is on extracting the projective shape information or the similarity shape from digital camera images or Magnetic Resonance Imaging(MRI). The...
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...
For many biomedical, environmental and economic studies with an unknown non-linear relationship between the response and its multiple predictors, a single index model provides practical dimension reduction and good physical...
Existing cure-rate survival models are generally not convenient for modeling and estimating the survival quantiles of a patient with specified covariate values. They also do not allow inference on the change in the number of clonogens...
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...
Meta-analysis has been a popular statistical tool to combine scientific findings from multiple studies addressing the same topic. Publication bias and heterogeneity are two critical issues that may influence the validity of the...
Statistical tests for biomarker identification and classification methods for patient grouping are two important topics in adaptive designs of clinical trials. In this article, we evaluate four test methods for biomarker identification:...
ROC curves are often used to evaluate predictive accuracy of statistical prediction models. This thesis studies other measures which not only incorporate the statistical but also the clinical consequences of using a particular prediction...
This dissertation presents some topics in spatial statistics and their application in biostatistics and environmental statistics. The field of spatial statistics is an energetic area in statistics. In Chapter 2 and Chapter 3, the goal is...
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.