Multiple Testing

A constructive approach to selective risk control

Many modern applications require the use of data to both select the statistical tasks and make valid inference after selection. In this article, we provide a unifying approach to control for a class of selective risks. Our method is motivated by a …

Simultaneous hypothesis testing using negative controls

Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (“negative result”) is expected. Motivated by a real proteomic dataset and an ad hoc procedure shared with us by …

Simultaneous Hypothesis Testing Using Internal Negative Controls with An Application to Proteomics

Negative control is a common technique in scientific investigations and broadly refers to the situation where a null effect (“negative result”) is expected. Motivated by a real proteomic dataset, we will present three promising and closely connected …

Multiple conditional randomization tests

We propose a general framework for (multiple) conditional randomization tests that incorporate several important ideas in the recent literature. We establish a general sufficient condition on the construction of multiple conditional randomization …

Using sparsity to overcome unmeasured confounding

Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. This talk will explore another aspect—the utility of sparsity in model identifiability through two problems …

Using sparsity to overcome unmeasured confounding: Two examples

Sparsity is often used to improve the interpretability of a statistical analysis and/or reduce the variance of a statistical estimator. This talk will explore another aspect—the utility of sparsity in model identifiability through two problems …

Multiple testing when many $p$-values are uniformly conservative

Qualitative interaction is an extreme form of treatment effect heterogeneity where the treatment can be beneficial for some but harmful for others. We formulated this question as a global testing problem with many conservative null $p$-values and …

Cross-screening in observational studies that test many hypotheses

This paper proposes a new method called 'cross-screening' to increase the power of sensitivity analysis when multiple causal hypotheses need to be tested simultaneously.

Confounder adjustment in multiple hypothesis testing

Confounding introduces hidden bias to the statistical inference. We show in modern simultaneous testing, it is possible to correct for unmeasured confounders. Previous methods including SVA, LEAPP, RUV are unified in the same framework in this paper. …