36 citations · 56 across the 21 of their papers we have counts for
7 papers · 1 filter
Dynamic Treatment Effects under Functional Longitudinal Studies
Andrew Ying
Establishing causality is a fundamental goal in fields like medicine and social sciences. While randomized controlled trials are the gold standard for causal inference, they are no…
Deepening the Understanding of Double Robustness Geometrically
Andrew Ying
Double robustness (DR) is a widely-used property of estimators that provides protection against model misspecification and slow convergence of nuisance functions. Despite its wides…
On Defense of the Hazard Ratio
Andrew Ying, Ronghui Xu
In this short communication, we describe the recent debate on whether the hazard function should be used for causal inference in time-to-event studies and consider three different…
Asymptotic Theory for Doubly Robust Estimators with Continuous-Time Nuisance Parameters
Andrew Ying
Doubly robust estimators have gained widespread popularity in various fields due to their ability to provide unbiased estimates under model misspecification. However, the asymptoti…
On the Asymptotic Distribution of the Scan Statistic for Empirical Distributions
Andrew Ying, Wen-Xin Zhou
We investigate the asymptotic behavior of several variants of the scan statistic applied to empirical distributions, which can be applied to detect the presence of an anomalous int…
A Scan Procedure for Multiple Testing
Shiyun Chen, Andrew Ying, Ery Arias-Castro
In a multiple testing framework, we propose a method that identifies the interval with the highest estimated false discovery rate of P-values and rejects the corresponding null hyp…
Detection of Sparse Mixtures: Higher Criticism and Scan Statistic
Ery Arias-Castro, Andrew Ying
We consider the problem of detecting a sparse mixture as studied by Ingster (1997) and Donoho and Jin (2004). We consider a wide array of base distributions. In particular, we stud…