10 citations · 13 across the 6 of their papers we have counts for
6 papers
Power-Enhanced Two-Sample Mean Tests for High-Dimensional Compositional Data with Application to Microbiome Data Analysis
Danning Li, Lingzhou Xue, Haoyi Yang +1
Testing differences in mean vectors is a fundamental task in the analysis of high-dimensional compositional data. Existing methods may suffer from low power if the underlying signa…
A Unified Combination Framework for Dependent Tests with Applications to Microbiome Association Studies
Xiufan Yu, Linjun Zhang, Arun Srinivasan +2
We introduce a novel meta-analysis framework to combine dependent tests under a general setting, and utilize it to synthesize various microbiome association tests that are calculat…
Generalized Causal Tree for Uplift Modeling
Preetam Nandy, Xiufan Yu, Wanjun Liu +3
Uplift modeling is crucial in various applications ranging from marketing and policy-making to personalized recommendations. The main objective is to learn optimal treatment alloca…
Multiple-Splitting Projection Test for High-Dimensional Mean Vectors
Wanjun Liu, Xiufan Yu, Runze Li
We propose a multiple-splitting projection test (MPT) for one-sample mean vectors in high-dimensional settings. The idea of projection test is to project high-dimensional samples t…
Power-enhanced simultaneous test of high-dimensional mean vectors and covariance matrices with application to gene-set testing
Xiufan Yu, Danning Li, Lingzhou Xue +1
Power-enhanced tests with high-dimensional data have received growing attention in theoretical and applied statistics in recent years. Existing tests possess their respective high-…
Fisher's combined probability test for high-dimensional covariance matrices
Xiufan Yu, Danning Li, Lingzhou Xue
Testing large covariance matrices is of fundamental importance in statistical analysis with high-dimensional data. In the past decade, three types of test statistics have been stud…