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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.
Low-rank matrix approximation is extremely useful in the analysis of data that arises in scientific computing, engineering applications, and data science. However, as data sizes grow, traditional low-rank matrix approximation methods, ...
Randomized quasi-Monte Carlo methods have been shown to offer estimates with smaller variances compared with estimates obtained with Monte Carlo. This dissertation examines the application of randomized quasi-Monte Carlo methods in the...
Bayesian additive regression trees (BART) are a Bayesian machine learning tool for nonparametric function estimation, which has been shown to have outstanding performance in terms of variable selection and prediction accuracy. Unmodified...
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...
A longitudinal study is a research design that collects observations measured repeatedly from particular individuals over prolonged periods of time. Nowadays, longitudinal studies are widely used in health sciences, social science, ...
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...
Multivariate linear models are commonly used for modeling the relationships between multiple responses and covariates. With OLS estimators, one can interpret the results in the analysis. However, the coefficient OLS estimates sometimes...
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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