Plugin estimation of smooth optimal transport maps Page 1 The Annals of Statistics 2024, Vol.
52, No. 3, 966–998 https://doi.org/10.1214/24-AOS2379 © Institute of Mathematical Statistics …

Mathematical optimization has always been at the heart of engineering, statistics, and
economics. In these applied domains, optimization concepts and methods have often been …

We study optimization for data-driven decision making when we have observations of the
uncertain parameters within an optimization model together with concurrent observations of …

We consider the problem of nonparametric regression under shape constraints. The main
examples include isotonic regression (with respect to any partial order), unimodal/convex …

Statistical models defined by shape constraints are a valuable alternative to parametric
models or nonparametric models defined in terms of quantitative smoothness constraints …

Nonparametric regression subject to convexity or concavity constraints is increasingly
popular in economics, finance, operations research, machine learning, and statistics …

Many modern statistical estimation problems are defined by three major components: a
statistical model that postulates the dependence of an output variable on the input features; …

The curse of dimensionality problem arises when a limited number of observations are used
to estimate a high-dimensional frontier, in particular, by data envelopment analysis (DEA) …

Max-affine regression refers to a model where the unknown regression function is modeled
as a maximum of unknown affine functions for a fixed. This generalizes linear regression …

Convex quantile regression (CQR) is a fully nonparametric approach to estimating quantile
functions, which has proved useful in many applications of productivity and efficiency …