5 papers
Adaptive Long-Run Variance Thresholding for Sparse Covariance Estimation in High-Dimensional Time Series
Wenhao Zhang, Zhaoxing Gao
Estimating a sparse covariance matrix is a fundamental problem in high-dimensional statistics. However, thresholding methods developed for independent data are generally not direct…
Forward Regression via Gram-Schmidt Orthogonalization for Ultra-High Dimensional Linear Models
Jialuo Chen, Zhaoxing Gao, Yifan Jiang +1
Forward regression is a classical and effective tool for variable screening in ultra-high dimensional linear models, but its standard projection-based implementation can be computa…
Modeling High-Dimensional Dependent Data in the Presence of Many Explanatory Variables and Weak Signals
Zhaoxing Gao, Ruey S. Tsay
This article considers a novel and widely applicable approach to modeling high-dimensional dependent data when a large number of explanatory variables are available and the signal-…
Denoising and Multilinear Projected-Estimation of High-Dimensional Matrix-Variate Factor Time Series
Zhaoxing Gao, Ruey S. Tsay
This paper proposes a new multi-linear projection method for denoising and estimation of high-dimensional matrix-variate factor time series. It assumes that a matri…
Optimal Bias-Correction and Valid Inference in High-Dimensional Ridge Regression: A Closed-Form Solution
Zhaoxing Gao, Ruey S. Tsay
Ridge regression is an indispensable tool in big data analysis. Yet its inherent bias poses a significant and longstanding challenge, compromising both statistical efficiency and s…