15 citations · 18 across the 13 of their papers we have counts for
5 papers · 1 filter
A Heavily Right Strategy for Statistical Inference with Dependent Studies in Arbitrary Dimensions
Tianle Liu, Xiao-Li Meng, Natesh S. Pillai
We leverage recent advances in heavy-tail approximations for global hypothesis testing with dependent studies to construct approximate confidence regions without modeling or estima…
Parallel Markov Chain Monte Carlo via Spectral Clustering
Guillaume W. Basse, Natesh S. Pillai, Aaron Smith
As it has become common to use many computer cores in routine applications, finding good ways to parallelize popular algorithms has become increasingly important. In this paper, we…
More Powerful Multiple Testing in Randomized Experiments with Non-Compliance
Joseph J. Lee, Laura Forastiere, Luke Miratrix +1
Two common concerns raised in analyses of randomized experiments are (i) appropriately handling issues of non-compliance, and (ii) appropriately adjusting for multiple tests (e.g.,…
Gaussian Process Regression with Location Errors
Daniel Cervone, Natesh S. Pillai
In this paper, we investigate Gaussian process regression models where inputs are subject to measurement error. In spatial statistics, input measurement errors occur when the geogr…
Causal inference from factorial designs using the potential outcomes model
Tirthankar Dasgupta, Natesh S. Pillai, Donald B. Rubin
A framework for causal inference from two-level factorial designs is proposed. The framework utilizes the concept of potential outcomes that lies at the center stage of causal infe…