We consider the problem of testing for a difference in means between clusters of
observations identified via k-means clustering. In this setting, classical hypothesis tests lead …

Suppose we observe a random vector X from some distribution in a known family with
unknown parameters. We ask the following question: when is it possible to split X into two …

The graph fused lasso—which includes as a special case the one-dimensional fused lasso—
is widely used to reconstruct signals that are piecewise constant on a graph, meaning that …

We introduce a pivot for exact selective inference with randomization. Not only does our
pivot lead to exact inference in Gaussian regression models, but it is also available in closed …

Our goal is to develop a general strategy to decompose a random variable X into multiple
independent random variables, without sacrificing any information about unknown …

Causal discovery and causal effect estimation are two fundamental tasks in causal
inference. While many methods have been developed for each task individually, statistical …

Post-selection inference via algorithmic stability Page 1 The Annals of Statistics 2023, Vol. 51,
No. 4, 1666–1691 https://doi.org/10.1214/23-AOS2303 © Institute of Mathematical Statistics …

Selective inference methods are developed for group lasso estimators for use with a wide
class of distributions and loss functions. The method includes the use of exponential family …

Purpose Frameworks for selecting exposures in high-dimensional environmental datasets,
while considering confounding, are lacking. We present a two-step approach for exposure …

We propose data thinning, an approach for splitting an observation into two or more
independent parts that sum to the original observation, and that follow the same distribution …