63 citations · 155 across the 12 of their papers we have counts for
14 papers
Subdata Selection for Measurement-Constrained Linear Regression under Model Misspecification
Yundi Kong, Min Yang, Ping-Shou Zhong
In many modern applications, obtaining response variables is costly, time-consuming, or technically challenging, making it impractical to measure outcomes for all available units.…
High-dimensional Gaussian Graphical Model Testing for Long-Memory Time Series
Percy S. Zhai, Ping-Shou Zhong, Wei Biao Wu
Many real-world high-dimensional time series exhibit long-memory, but Gaussian graphical model testing in this regime remains understudied. We develop a direct, data-adaptive test…
Functional varying-coefficient model under heteroskedasticity with application to DTI data
Pratim Guha Niyogi, Ping-Shou Zhong, Xiaohong Joe Zhou
In this paper, we develop a multi-step estimation procedure to simultaneously estimate the varying-coefficient functions using a local-linear generalized method of moments (GMM) ba…
Flexible Inference of Optimal Individualized Treatment Strategy in Covariate Adjusted Randomization with Multiple Covariates
Trinetri Ghosh, Yanyuan Ma, Rui Song +1
To maximize clinical benefit, clinicians routinely tailor treatment to the individual characteristics of each patient, where individualized treatment rules are needed and are of si…
High-dimensional Precision Matrix Estimation with a Known Graphical Structure
Thien-Minh Le, Ping-Shou Zhong
A precision matrix is the inverse of a covariance matrix. In this paper, we study the problem of estimating the precision matrix with a known graphical structure under high-dimensi…
Asymptotic independence of spiked eigenvalues and linear spectral statistics for large sample covariance matrices
Zhixiang Zhang, Shurong Zheng, Guangming Pan +1
We consider general high-dimensional spiked sample covariance models and show that their leading sample spiked eigenvalues and their linear spectral statistics are asymptotically i…
Homogeneity Tests of Covariance and Change-Points Identification for High-Dimensional Functional Data
Shawn Santo, Ping-Shou Zhong
We consider inference problems for high-dimensional (HD) functional data with a dense number (T) of repeated measurements taken for a large number of p variables from a small numbe…
Change Point Detection in the Mean of High-Dimensional Time Series Data under Dependence
Jun Li, Minya Xu, Ping-Shou Zhong +1
High-dimensional time series are characterized by a large number of measurements and complex dependence, and often involve abrupt change points. We propose a new procedure to detec…
Uncertainty Estimation in Functional Linear Models
Tapabrata Maiti, Abolfazl Safikhani, Ping-Shou Zhong
Functional data analysis is proved to be useful in many scientific applications. The physical process is observed as curves and often there are several curves observed due to multi…
Test for Temporal Homogeneity of Means in High-dimensional Longitudinal Data
Ping-Shou Zhong, Jun Li
This paper considers the problem of testing temporal homogeneity of -dimensional population mean vectors from the repeated measurements of subjects over times. To cope w…
Two-Sample Tests for High Dimensional Means with Thresholding and Data Transformation
Song Xi Chen, Jun Li, Ping-Shou Zhong
We consider testing for two-sample means of high dimensional populations by thresholding. Two tests are investigated, which are designed for better power performance when the two p…
A rate optimal procedure for sparse signal recovery under dependence
Jun Li, Ping-Shou Zhong
The paper considers the problem of identifying the sparse different components between two high dimensional means of column-wise dependent random vectors. We show that the dependen…
Tests alternative to higher criticism for high-dimensional means under sparsity and column-wise dependence
Ping-Shou Zhong, Song Xi Chen, Minya Xu
We consider two alternative tests to the Higher Criticism test of Donoho and Jin [Ann. Statist. 32 (2004) 962-994] for high-dimensional means under the sparsity of the nonzero mean…
ANOVA for longitudinal data with missing values
Song Xi Chen, Ping-Shou Zhong
We carry out ANOVA comparisons of multiple treatments for longitudinal studies with missing values. The treatment effects are modeled semiparametrically via a partially linear regr…