21 citations · 24 across the 5 of their papers we have counts for
5 papers
Clustering High-dimensional Data via Feature Selection
Tianqi Liu, Yu Lu, Biqing Zhu +1
High-dimensional clustering analysis is a challenging problem in statistics and machine learning, with broad applications such as the analysis of microarray data and RNA-seq data.…
Variance Estimation and Confidence Intervals from High-dimensional Genome-wide Association Studies Through Misspecified Mixed Model Analysis
Cecilia Dao, Jiming Jiang, Debashis Paul +1
We study variance estimation and associated confidence intervals for parameters characterizing genetic effects from genome-wide association studies (GWAS) misspecified mixed model…
Characterizing Spatiotemporal Transcriptome of Human Brain via Low Rank Tensor Decomposition
Tianqi Liu, Ming Yuan, Hongyu Zhao
Spatiotemporal gene expression data of the human brain offer insights on the spa- tial and temporal patterns of gene regulation during brain development. Most existing methods for…
Ultrahigh Dimensional Feature Selection via Kernel Canonical Correlation Analysis
Tianqi Liu, Kuang-Yao Lee, Hongyu Zhao
High-dimensional variable selection is an important issue in many scientific fields, such as genomics. In this paper, we develop a sure independence feature screening pro- cedure b…
Estimation for ultra-high dimensional factor model: a pivotal variable detection based approach
Junlong Zhao, Hongyu Zhao, Lixing Zhu
For factor model, the involved covariance matrix often has no row sparse structure because the common factors may lead some variables to strongly associate with many others. Under…