Dept. of Statistics
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(2018-12-04)In part 1, we propose a pointwise inference algorithm for high-dimensional linear models with time-varying coefficients and dependent error processes. The method is based on a novel combination of the nonparametric kernel ...
(2018-04-17)This dissertation is devoted to statistical inference based on characteristic functions. For some popular stochastic processes (e.g., Lévy processes, Lévy driven Ornstein-Uhlenbeck processes), the transition density may ...
(2018-03-28)Learning sparsity pattern in high dimension is a great challenge in both implementation and theory. In this thesis we develop scalable Bayesian algorithms based on EM algorithm and variational inference to learn sparsity ...
(2017-07-14)The study of dependence for high dimensional data originates in many different areas of contemporary research. While a lot of existing work focuses on measuring the linear dependence and monotone dependence for fixed ...
(2017-07-13)Individualized modeling and multi-modality data integration have experienced an explosive growth in recent years, which have many important applications in biomedical research, personalized education and marketing. ...