75 citations · 160 across the 5 of their papers we have counts for
5 papers
Principal component analysis for high-dimensional compositional data
Jingru Zhang, Wei Lin
Dimension reduction for high-dimensional compositional data plays an important role in many fields, where the principal component analysis of the basis covariance matrix is of scie…
SOFAR: large-scale association network learning
Yoshimasa Uematsu, Yingying Fan, Kun Chen +2
Many modern big data applications feature large scale in both numbers of responses and predictors. Better statistical efficiency and scientific insights can be enabled by understan…
Large Covariance Estimation for Compositional Data via Composition-Adjusted Thresholding
Yuanpei Cao, Wei Lin, Hongzhe Li
High-dimensional compositional data arise naturally in many applications such as metagenomic data analysis. The observed data lie in a high-dimensional simplex, and conventional st…
Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics
Wei Lin, Rui Feng, Hongzhe Li
In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimension…
High-Dimensional Sparse Additive Hazards Regression
Wei Lin, Jinchi Lv
High-dimensional sparse modeling with censored survival data is of great practical importance, as exemplified by modern applications in high-throughput genomic data analysis and cr…