We introduce a simple new approach to variable selection in linear regression, with a
particular focus on quantifying uncertainty in which variables should be selected. The …

In the statistical analysis of genome-wide association data, it is challenging to precisely
localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize …

We introduce a method to draw causal inferences—inferences immune to all possible
confounding—from genetic data that include parents and offspring. Causal conclusions are …

High-dimensional group inference is an essential part of statistical methods for analysing
complex data sets, including hierarchical testing, tests of interaction, detection of …

We consider post-selection inference for high-dimensional (generalized) linear models.
Data carving from Fithian, Sun and Taylor [10] is a promising technique to perform this task …

We consider problems where many, somewhat redundant, hypotheses are tested and we
are interested in reporting the most precise rejections, with false discovery rate (FDR) …

Background Heritability is a central measure in genetics quantifying how much of the
variability observed in a trait is attributable to genetic differences. Existing methods for …

The identification of prognostic and predictive biomarker signatures is crucial for drug
development and providing personalized treatment to cancer patients. However, the …

We discuss the problem of supervised statistical learning in large data sets where the
number of explanatory variables may exceed the number of observations. In this situation …

Scientists often must simultaneously localize and discover signals. For instance, in genetic
fine-mapping, high correlations between nearby genetic variants make it hard to identify the …