We discuss inference after data exploration, with a particular focus on inference after model
or variable selection. We review three popular approaches to this problem: sample splitting …

We consider the problem of providing valid inference for a selected parameter in a sparse
regression setting. It is well known that classical regression tools can be unreliable in this …

This article concerns the potential bias in statistical inference on treatment effects when a
large number of covariates are present in a linear or partially linear model. While the …

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

In this paper we adopt the familiar sparse, high-dimensional linear regression model and
focus on the important but often overlooked task of prediction. In particular, we consider a …

A conditioning on the event of having selected one model from a set of possibly misspecified
normal linear regression models leads to the construction of uniformly optimal conditional …

Background Empirical studies have demonstrated associations between ten original
adverse childhood experiences (ACEs) and multiple health outcomes. Identifying expanded …

It is well known that with observational data, models used in conventional regression
analyses are commonly misspecified. Yet in practice, one tends to proceed with …

High-dimensional data are increasingly popular in various physical, biological and social
disciplines. A common existing approach of repeatedly splitting data was suggested to …

Machine learning models that are overfitted/overtrained are more vulnerable to knowledge
leakage, which poses a risk to privacy. Suppose we download or receive a model from a …