Specifying, assessing, and selecting among candidate statistical models is fundamental to
ecological research. Commonly used approaches to model selection are based on …

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 …

Background: Intersectionality theoretical frameworks have been increasingly incorporated
into quantitative research. A range of methods have been applied to describing outcomes …

Supplement to “Bootstrapping and sample splitting for high-dimensional, assumption-lean
inference”. This supplement provides additional material, including numerical examples …

Policy makers, firms, and researchers often choose among multiple options based on
estimates. Sampling error in the estimates used to guide choice leads to a winner's curse …

Uniform asymptotic inference and the bootstrap after model selection Page 1 The Annals of
Statistics 2018, Vol. 46, No. 3, 1255–1287 https://doi.org/10.1214/17-AOS1584 © Institute of …

The research on statistical inference after data-driven model selection can be traced as far
back as Koopmans (1949). The intensive research on modern model selection methods for …

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 …

Although there is a vast body of literature related to methods for detecting change-points
(CPs), less attention has been paid to assessing the statistical reliability of the detected CPs …

Ignoring the model selection step in inference after selection is harmful. In this paper we
study the asymptotic distribution of estimators after model selection using the Akaike …