4k citations · 6.4k across the 63 of their papers we have counts for
16 papers · 1 filter
CP Factor Model for Dynamic Tensors
Yuefeng Han, Dan Yang, Cun-Hui Zhang +1
Observations in various applications are frequently represented as a time series of multidimensional arrays, called tensor time series, preserving the inherent multidimensional str…
Simultaneous Decorrelation of Matrix Time Series
Yuefeng Han, Rong Chen, Cun-Hui Zhang +1
We propose a contemporaneous bilinear transformation for a matrix time series to alleviate the difficulties in modeling and forecasting matrix time series when and/…
Rank Determination in Tensor Factor Model
Yuefeng Han, Rong Chen, Cun-Hui Zhang
Factor model is an appealing and effective analytic tool for high-dimensional time series, with a wide range of applications in economics, finance and statistics. This paper develo…
Tensor Factor Model Estimation by Iterative Projection
Yuefeng Han, Rong Chen, Dan Yang +1
Tensor time series, which is a time series consisting of tensorial observations, has become ubiquitous. It typically exhibits high dimensionality. One approach for dimension reduct…
Factor Models for High-Dimensional Tensor Time Series
Rong Chen, Dan Yang, Cun-hui Zhang
Large tensor (multi-dimensional array) data are now routinely collected in a wide range of applications, due to modern data collection capabilities. Often such observations are tak…
Beyond Gaussian Approximation: Bootstrap for Maxima of Sums of Independent Random Vectors
Hang Deng, Cun-Hui Zhang
The Bonferroni adjustment, or the union bound, is commonly used to study rate optimality properties of statistical methods in high-dimensional problems. However, in practice, the B…
Theory of the GMM Kernel
Ping Li, Cun-Hui Zhang
We develop some theoretical results for a robust similarity measure named "generalized min-max" (GMM). This similarity has direct applications in machine learning as a positive def…
High-dimensional simultaneous inference with the bootstrap
Ruben Dezeure, Peter Bühlmann, Cun-Hui Zhang
We propose a residual and wild bootstrap methodology for individual and simultaneous inference in high-dimensional linear models with possibly non-Gaussian and heteroscedastic erro…
Group-Linear Empirical Bayes Estimates for a Heteroscedastic Normal Mean
Asaf Weinstein, Zhuang Ma, Lawrence D. Brown +1
The problem of estimating the mean of a normal vector with known but unequal variances introduces substantial difficulties that impair the adequacy of traditional empirical Bayes e…
Compressed Sensing with Very Sparse Gaussian Random Projections
Ping Li, Cun-Hui Zhang
We study the use of very sparse random projections for compressed sensing (sparse signal recovery) when the signal entries can be either positive or negative. In our setting, the e…
Sparse Recovery with Very Sparse Compressed Counting
Ping Li, Cun-Hui Zhang, Tong Zhang
Compressed sensing (sparse signal recovery) often encounters nonnegative data (e.g., images). Recently we developed the methodology of using (dense) Compressed Counting for recover…
Semi-Penalized Inference with Direct False Discovery Rate Control in High-Dimensions
Jian Huang, Shuangge Ma, Cun-Hui Zhang +1
We propose a new method, semi-penalized inference with direct false discovery rate control (SPIDR), for variable selection and confidence interval construction in high-dimensional…
Compressed Counting Meets Compressed Sensing
Ping Li, Cun-Hui Zhang, Tong Zhang
Compressed sensing (sparse signal recovery) has been a popular and important research topic in recent years. By observing that natural signals are often nonnegative, we propose a n…
Correlated variables in regression: clustering and sparse estimation
Peter Bühlmann, Philipp Rütimann, Sara van de Geer +1
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as…
Confidence Intervals for Low-Dimensional Parameters in High-Dimensional Linear Models
Cun-Hui Zhang, Stephanie S. Zhang
The purpose of this paper is to propose methodologies for statistical inference of low-dimensional parameters with high-dimensional data. We focus on constructing confidence interv…
Empirical Bayes methods for controlling the false discovery rate with dependent data
Weihua Tang, Cun-Hui Zhang
False discovery rate (FDR) has been widely used as an error measure in large scale multiple testing problems, but most research in the area has been focused on procedures for contr…