82 citations · 384 across the 49 of their papers we have counts for
15 papers · 1 filter
Mode-wise Principal Subspace Pursuit and Matrix Spiked Covariance Model
Runshi Tang, Ming Yuan, Anru R. Zhang
This paper introduces a novel framework called Mode-wise Principal Subspace Pursuit (MOP-UP) to extract hidden variations in both the row and column dimensions for matrix data. To…
Core Shrinkage Covariance Estimation for Matrix-variate Data
Peter Hoff, Andrew McCormack, Anru R. Zhang
A separable covariance model for a random matrix provides a parsimonious description of the covariances among the rows and among the columns of the matrix, and permits likelihood-b…
Guaranteed Functional Tensor Singular Value Decomposition
Rungang Han, Pixu Shi, Anru R. Zhang
This paper introduces the functional tensor singular value decomposition (FTSVD), a novel dimension reduction framework for tensors with one functional mode and several tabular mod…
Exact Clustering in Tensor Block Model: Statistical Optimality and Computational Limit
Rungang Han, Yuetian Luo, Miaoyan Wang +1
High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex a…
Learning Markov models via low-rank optimization
Ziwei Zhu, Xudong Li, Mengdi Wang +1
Modeling unknown systems from data is a precursor of system optimization and sequential decision making. In this paper, we focus on learning a Markov model from a single trajectory…
High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis
Pixu Shi, Yuchen Zhou, Anru R. Zhang
In microbiome and genomic studies, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes…
Nonparametric covariance estimation for mixed longitudinal studies, with applications in midlife women's health
Anru R. Zhang, Kehui Chen
In mixed longitudinal studies, a group of subjects enter the study at different ages (cross-sectional) and are followed for successive years (longitudinal). In the context of such…
Multi-sample Estimation of Bacterial Composition Matrix in Metagenomics Data
Yuanpei Cao, Anru Zhang, Hongzhe Li
Metagenomics sequencing is routinely applied to quantify bacterial abundances in microbiome studies, where the bacterial composition is estimated based on the sequencing read count…
Cross: Efficient Low-rank Tensor Completion
Anru Zhang
The completion of tensors, or high-order arrays, attracts significant attention in recent research. Current literature on tensor completion primarily focuses on recovery from a set…
Semi-supervised Inference: General Theory and Estimation of Means
Anru Zhang, Lawrence D. Brown, T. Tony Cai
We propose a general semi-supervised inference framework focused on the estimation of the population mean. As usual in semi-supervised settings, there exists an unlabeled sample of…
Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data
T. Tony Cai, Anru Zhang
Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations…
Structured Matrix Completion with Applications to Genomic Data Integration
Tianxi Cai, T. Tony Cai, Anru Zhang
Matrix completion has attracted significant recent attention in many fields including statistics, applied mathematics and electrical engineering. Current literature on matrix compl…
Inference for High-dimensional Differential Correlation Matrices
T. Tony Cai, Anru Zhang
Motivated by differential co-expression analysis in genomics, we consider in this paper estimation and testing of high-dimensional differential correlation matrices. An adaptive th…
Instrumental Variables Estimation with Some Invalid Instruments and its Application to Mendelian Randomization
Hyunseung Kang, Anru Zhang, T. Tony Cai +1
Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the…
Methods to Calculate the Upper Bound of Gini Coefficient Based on Grouped Data and the Result for China
Pixu Shi, Anru R. Zhang
Determining an upper bound, particularly the optimal upper bound of the Gini coefficient when dealing with grouped data without specified income brackets, remains an important and…