We derive the asymptotic distributions of the spiked eigenvalues and eigenvectors under a
generalized and unified asymptotic regime, which takes into account the magnitude of …

We propose a general Principal Orthogonal complEment Thresholding (POET) framework
for large-scale covariance matrix estimation based on the approximate factor model. A set of …

High-dimensional multi-source data are encountered in many fields. Despite recent
developments on the integrative dimension reduction of such data, most existing methods …

This work introduces a novel framework for dynamic factor model‐based group‐level
analysis of multiple subjects time‐series data, called GRoup Integrative DYnamic factor …

This thesis focuses on two separate topics in modeling of high-dimensional time series
(HDTS) with several structures and their various applications. The first topic is on modeling …

Functional magnetic resonance imaging (fMRI) data have become increasingly available
and are useful for describing functional connectivity (FC), the relatedness of neuronal activity …

In modern statistics, interests shift from pursuing the uniformly minimum variance unbiased
estimator to reducing mean squared error (MSE) or residual squared error. Shrinkage-based …

In modern scientific research, data heterogeneity is commonly observed owing to the
abundance of complex data. We propose a factor regression model for data with …

Recent technological developments give researchers the opportunity to obtain large
informative datasets when studying infectious disease. Such datasets are often high …

Functional magnetic resonance imaging (fMRI) data is increasingly available and provides
insight into the physiological mechanisms of the brain. As psychiatric disorders and many …