# Nonparametric estimation via partial derivatives

## TL;DR

This paper introduces a new nonparametric estimation method using partial derivatives that achieves faster convergence rates in high-dimensional settings.

## Contribution

The novel approach uses partial derivatives to achieve near-parametric convergence rates in nonparametric estimation.

## Key findings

- The method achieves optimal rates with gradient information equal to those without gradients in interaction models.
- Additive models using gradient information reach the parametric rate of n.
- Theoretical results are validated through synthetic and real data applications.

## Abstract

Traditional nonparametric estimation methods often lead to a slow convergence rate in large dimensions and require unrealistically large dataset sizes for reliable conclusions. We develop an approach based on partial derivatives, either observed or estimated, to effectively estimate the function at near-parametric convergence rates. This novel approach and computational algorithm could lead to methods useful to practitioners in many areas of science and engineering. Our theoretical results reveal behaviour universal to this class of nonparametric estimation problems. We explore a general setting involving tensor product spaces and build upon the smoothing spline analysis of variance framework. For d-dimensional models under full interaction, the optimal rates with gradient information on p covariates are identical to those for the (d−p)-interaction models without gradients and, therefore, the models are immune to the curse of interaction. For additive models, the optimal rates using gradient information are n, thus achieving the parametric rate. We demonstrate aspects of the theoretical results through synthetic and real data applications.