activity
20172021
most citedIdentifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure

4 citations · 6 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME2021

Linear shrinkage for predicting responses in large-scale multivariate linear regression

Yihe Wang, Sihai Dave Zhao

We propose a new prediction method for multivariate linear regression problems where the number of features is less than the sample size but the number of outcomes is extremely lar…

cs.LG20212 cited

Capturing patterns of variation unique to a specific dataset

Robin Tu, Alexander H. Foss, Sihai D. Zhao

Capturing patterns of variation present in a dataset is important in exploratory data analysis and unsupervised learning. Contrastive dimension reduction methods, such as contrasti…

stat.ME2019

Estimation and inference for the indirect effect in high-dimensional linear mediation models

Ruixuan Rachel Zhou, Liewei Wang, Sihai Dave Zhao

Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in…

stat.ME2019

Simultaneous estimation of normal means with side information

Sihai Dave Zhao

The integrative analysis of multiple datasets is an important strategy in data analysis. It is increasingly popular in genomics, which enjoys a wealth of publicly available dataset…

stat.ML20174 cited

Identifying Genetic Risk Factors via Sparse Group Lasso with Group Graph Structure

Tao Yang, Paul Thompson, Sihai Zhao +1

Genome-wide association studies (GWA studies or GWAS) investigate the relationships between genetic variants such as single-nucleotide polymorphisms (SNPs) and individual traits. R…