In this paper, we survey the most recent advances in supervised machine learning (ML) and
high‐dimensional models for time‐series forecasting. We consider both linear and nonlinear …

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

In this monograph, we have attempted to summarize the actively develo** field of
statistical learning with sparsity. A sparse statistical model is one having only a small …

Selecting an appropriate set of confounders for which to control is critical for reliable causal
inference. Recent theoretical and methodological developments have helped clarify a …

We propose the elastic net, a new regularization and variable selection method. Real world
data and a simulation study show that the elastic net often outperforms the lasso, while …

This paper discusses two important limitations of the common practice of testing for
preexisting differences in trends (“pre-trends”) when using difference-in-differences and …

We develop a general framework for distribution-free predictive inference in regression,
using conformal inference. The proposed methodology allows for the construction of a …

Many contemporary large-scale applications involve building interpretable models linking a
large set of potential covariates to a response in a non-linear fashion, such as when the …

Some empirical results are more likely to be published than others. Selective publication
leads to biased estimates and distorted inference. We propose two approaches for …

This paper proposes tools for robust inference for difference-in-differences and eventstudy
designs. Instead of requiring that the parallel trends assumption holds exactly, we impose …