31 citations · 46 across the 13 of their papers we have counts for
13 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-…
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…
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…
Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors
Zhaoxing Gao, Ruey S. Tsay
This paper proposes a novel dynamic forecasting method using a new supervised Principal Component Analysis (PCA) when a large number of predictors are available. The new supervised…
Determination of the effective cointegration rank in high-dimensional time-series predictive regressions
Puyi Fang, Zhaoxing Gao, Ruey S. Tsay
This paper proposes a new approach to identifying the effective cointegration rank in high-dimensional unit-root (HDUR) time series from a prediction perspective using reduced-rank…
Divide-and-Conquer: A Distributed Hierarchical Factor Approach to Modeling Large-Scale Time Series Data
Zhaoxing Gao, Ruey S. Tsay
This paper proposes a hierarchical approximate-factor approach to analyzing high-dimensional, large-scale heterogeneous time series data using distributed computing. The new method…
A Two-Way Transformed Factor Model for Matrix-Variate Time Series
Zhaoxing Gao, Ruey S. Tsay
We propose a new framework for modeling high-dimensional matrix-variate time series by a two-way transformation, where the transformed data consist of a matrix-variate factor proce…
Modeling High-Dimensional Unit-Root Time Series
Zhaoxing Gao, Ruey S. Tsay
This paper proposes a new procedure to build factor models for high-dimensional unit-root time series by postulating that a -dimensional unit-root process is a nonsingular linea…
Segmenting High-dimensional Matrix-valued Time Series via Sequential Transformations
Zhaoxing Gao
Modeling matrix-valued time series is an interesting and important research topic. In this paper, we extend the method of Chang et al. (2017) to matrix-valued time series. For any…
Modeling High-Dimensional Time Series: A Factor Model with Dynamically Dependent Factors and Diverging Eigenvalues
Zhaoxing Gao, Ruey S. Tsay
This article proposes a new approach to modeling high-dimensional time series by treating a -dimensional time series as a nonsingular linear transformation of certain common fac…
A Structural-Factor Approach to Modeling High-Dimensional Time Series and Space-Time Data
Zhaoxing Gao, Ruey S Tsay
This paper considers a structural-factor approach to modeling high-dimensional time series and space-time data by decomposing individual series into trend, seasonal, and irregular…