36 citations · 96 across the 14 of their papers we have counts for
14 papers
High-Dimensional Assisted Learning for Vertically Distributed Data with Blockwise Missingness
Yuwen Long, Shuyuan Wu, Yin Xia
In multi-institutional studies, different parties hold distinct feature blocks for partially overlapping sets of individuals. Responses may also be missing for some records. In suc…
Statistical Inference and Large-scale Multiple Testing for High-dimensional Regression Models
T. Tony Cai, Zijian Guo, Yin Xia
This paper presents a selective survey of recent developments in statistical inference and multiple testing for high-dimensional regression models, including linear and logistic re…
A Decorrelating and Debiasing Approach to Simultaneous Inference for High-Dimensional Confounded Models
Yinrui Sun, Li Ma, Yin Xia
Motivated by the simultaneous association analysis with the presence of latent confounders, this paper studies the large-scale hypothesis testing problem for the high-dimensional c…
A Locally Adaptive Algorithm for Multiple Testing with Network Structure
Ziyi Liang, T. Tony Cai, Wenguang Sun +1
Incorporating auxiliary information alongside primary data can significantly enhance the accuracy of simultaneous inference. However, existing multiple testing methods face challen…
NAPA: Neighborhood-Assisted and Posterior-Adjusted Two-sample Inference
Li Ma, Yin Xia, Lexin Li
Two-sample multiple testing problems of sparse spatial data are frequently arising in a variety of scientific applications. In this article, we develop a novel neighborhood-assiste…
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems
Yingzhi Xia, Nicholas Zabaras
Estimation of spatially-varying parameters for computationally expensive forward models governed by partial differential equations is addressed. A novel multiscale Bayesian inferen…
Testing and Support Recovery of Correlation Structures for Matrix-Valued Observations with an Application to Stock Market Data
Xin Chen, Dan Yang, Yan Xu +3
Estimation of the covariance matrix of asset returns is crucial to portfolio construction. As suggested by economic theories, the correlation structure among assets differs between…
Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
Molei Liu, Yin Xia, Kelly Cho +1
Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensio…
Paired Test of Matrix Graphs and Brain Connectivity Analysis
Yuting Ye, Yin Xia, Lexin Li
Inferring brain connectivity network and quantifying the significance of interactions between brain regions are of paramount importance in neuroscience. Although there have recentl…
Hypothesis Testing for Network Data with Power Enhancement
Yin Xia, Lexin Li
Comparing two population means of network data is of paramount importance in a wide range of scientific applications. Many existing network inference solutions focus on global test…
A Normality Test for High-dimensional Data based on a Nearest Neighbor Approach
Hao Chen, Yin Xia
Many statistical methodologies for high-dimensional data assume the population is normal. Although a few multivariate normality tests have been proposed, to the best of our knowled…
Individual Data Protected Integrative Regression Analysis of High-dimensional Heterogeneous Data
Tianxi Cai, Molei Liu, Yin Xia
Evidence-based decision making often relies on meta-analyzing multiple studies, which enables more precise estimation and investigation of generalizability. Integrative analysis of…
Hypothesis Testing of Matrix Graph Model with Application to Brain Connectivity Analysis
Yin Xia, Lexin Li
Brain connectivity analysis is now at the foreground of neuroscience research. A connectivity network is characterized by a graph, where nodes represent neural elements such as neu…
Adaptive confidence intervals for regression functions under shape constraints
T. Tony Cai, Mark G. Low, Yin Xia
Adaptive confidence intervals for regression functions are constructed under shape constraints of monotonicity and convexity. A natural benchmark is established for the minimum exp…