5 papers
Power of masking methods for adaptive testing in a multivariate normal means problem
Abhinav Chakraborty, Junu Lee, Eugene Katsevich
Many large-scale testing procedures learn signal structure from the data to boost power. Direct data reuse can inflate Type-I error ("double dipping"), so a common remedy is maskin…
The conditional saddlepoint approximation for fast and accurate large-scale hypothesis testing
Ziang Niu, Jyotishka Ray Choudhury, Eugene Katsevich
Large-scale testing in modern applications such as genomics often entails a trade-off between accuracy and speed: multiplicity corrections push cutoffs deep into the tails, where n…
Doubly robust and computationally efficient high-dimensional variable selection
Abhinav Chakraborty, Jeffrey Zhang, Eugene Katsevich
Variable selection can be performed by testing conditional independence (CI) between each predictor and the response, given the other predictors. A doubly robust and powerful optio…
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…
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…