The random forest algorithm, proposed by L. Breiman in 2001, has been extremely
successful as a general-purpose classification and regression method. The approach, which …

This lively book lays out a methodology of confidence distributions and puts them through
their paces. Among other merits they lead to optimal combinations of confidence from …

Background Threshold regression models are a diverse set of non-regular regression
models that all depend on change points or thresholds. They provide a simple but elegant …

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random
Forest) is a widely used machine learning algorithm, due to its practical effectiveness and …

This article extends the scope of empirical likelihood methodology in three directions: to
allow for plug-in estimates of nuisance parameters in estimating equations, slower than n …

Emphasizing the inductive nature of statistical thinking, Environmental and Ecological
Statistics with R, Second Edition, connects applied statistics to the environmental and …

Random forests are among the most popular off-the-shelf supervised learning algorithms.
Despite their well-documented empirical success, however, until recently, few theoretical …

Divide and conquer in nonstandard problems and the super-efficiency phenomenon Page 1
The Annals of Statistics 2019, Vol. 47, No. 2, 720–757 https://doi.org/10.1214/17-AOS1633 © …

We propose a procedure to build a decision tree which approximates the performance of
complex machine learning models. This single approximation tree can be used to interpret …

Decision tree learning is increasingly being used for pointwise inference. Important
applications include causal heterogenous treatment effects and dynamic policy decisions …