This chapter describes several nonparametric estimation and testing methods for
econometric models. Instead of using parametric assumptions on the functions and …

When one wants to estimate a model without specifying the functions and distributions
parametrically, or when one wants to analyze the identification of a model independently of …

On the rate of convergence of fully connected deep neural network regression estimates Page
1 The Annals of Statistics 2021, Vol. 49, No. 4, 2231–2249 https://doi.org/10.1214/20-AOS2034 …

Abstract Evaluation of treatment effects and more general estimands is typically achieved via
parametric modeling, which is unsatisfactory since model misspecification is likely. Data …

On deep learning as a remedy for the curse of dimensionality in nonparametric regression
Page 1 The Annals of Statistics 2019, Vol. 47, No. 4, 2261–2285 https://doi.org/10.1214/18-AOS1747 …

Sufficient dimension reduction is a rapidly develo** research field that has wide
applications in regression diagnostics, data visualization, machine learning, genomics …

Data-analytic approaches to regression problems, arising from many scientific disciplines
are described in this book. The aim of these nonparametric methods is to relax assumptions …

This book provides the most comprehensive treatment to date of microeconometrics, the
analysis of individual-level data on the economic behavior of individuals or firms using …

This chapter discusses the innovations of additional flexible methods for modeling an
individual term in an additive model. It focuses on how we fit additive models. A general and …

Quantile regression is gradually emerging as a unified statistical methodology for estimating
models of conditional quantile functions. By complementing the exclusive focus of classical …