Single-parameter summaries of variable effects are desirable for ease of...
We study partially linear models in settings where observations are arra...
Many testing problems are readily amenable to randomised tests such as t...
Testing the significance of a variable or group of variables X for
predi...
Cross-validation is the standard approach for tuning parameter selection...
There are a variety of settings where vague prior information may be
ava...
Knowing the causal structure of a system is of fundamental interest in m...
We study the problem of testing the null hypothesis that X and Y are
con...
We consider estimation of average treatment effects given observational ...
We propose a method for estimation in high-dimensional linear models wit...
All models may be wrong—but that is not necessarily a problem for
infere...
We propose a family of tests to assess the goodness-of-fit of a
high-dim...
In this work we consider the problem of estimating a high-dimensional p
...
It is a common saying that testing for conditional independence, i.e.,
t...
When performing regression on a dataset with p variables, it is often of...
Large-scale regression problems where both the number of variables, p, a...