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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.
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...
With the increase in computation and data storage, there has been a vast collection of information gained with scientific measurement devices. However, with this increase in data and variety of domain applications, statistical...
In this dissertation, we study joint sparsity pursuit and its applications in variable selection in high dimensional data. The first part of dissertation focuses on hierarchical variable selection and its application in a two-way...
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...
In this study, we propose a robust method holding a selective shrinkage power for small area estimation with automatic random effects selection referred to as SARS. In our proposed model, both fixed effects and random effects are treated...
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...
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...
Dependence is one of the most important concepts in probability and statistics. To detect and measure dependency between response variables and predictors, various models have been constructed, from simple models like least square...
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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