5 papers
Computationally efficient goodness-of-fit tests through kernelized Stein discrepancy
Zhihan Huang, Ziang Niu
Models with intractable normalizing constants are widely used in statistics and machine learning. Assessing the adequacy of such models poses significant challenges: obtaining samp…
Assumption-lean weak limits and tests for two-stage adaptive experiments
Ziang Niu, Zhimei Ren
Adaptive experiments are becoming increasingly popular in real-world applications for effectively maximizing in-sample welfare and efficiency by data-driven sampling. Despite their…
The permuted score test for robust differential expression analysis
Timothy Barry, Ziang Niu, Eugene Katsevich +1
Negative binomial (NB) regression is a popular method for identifying differentially expressed genes in genomics data, such as bulk and single-cell RNA sequencing data. However, NB…
The saddlepoint approximation for averages of conditionally independent random variables
Ziang Niu, Jyotishka Ray Choudhury, Eugene Katsevich
Motivated by the application of saddlepoint approximations to resampling-based statistical tests, we prove that the Lugannani-Rice formula has vanishing relative error when applied…
Saddlepoint approximations for plug-in resampling
Ziang Niu, Zhihan Huang, Jyotishka Ray Choudhury +1
Resampling-based procedures can improve on normal approximations in sparse, large-scale testing problems, but their computational cost can be prohibitive. We recognize that several…