Asymptotic Equivalence for Nonparametric Regression

TL;DR

This paper demonstrates that a nonparametric regression model with independent observations can be asymptotically approximated by a Gaussian shift model, simplifying analysis and inference in such settings.

Contribution

It establishes the asymptotic equivalence between a nonparametric regression model and a Gaussian shift model under regularity conditions.

Findings

Model can be approximated by Gaussian shift model

Asymptotic equivalence holds under regularity assumptions

Facilitates simpler inference in nonparametric regression

Abstract

We consider a nonparametric model $\mathcal{E}^{n},$ generated by independent observations $X_{i},$ $i=1,...,n,$ with densities $p(x,\theta_{i}),$ $i=1,...,n,$ the parameters of which $\theta _{i}=f(i/n)\in \Theta $ are driven by the values of an unknown function $f:[0,1]\rightarrow \Theta $ in a smoothness class. The main result of the paper is that, under regularity assumptions, this model can be approximated, in the sense of the Le Cam deficiency pseudodistance, by a nonparametric Gaussian shift model $Y_{i}=\Gamma (f(i/n))+\varepsilon _{i},$ where $\varepsilon_{1},...,\varepsilon _{n}$ are i.i.d. standard normal r.v.'s, the function $\Gamma (\theta ):\Theta \rightarrow \mathrm{R}$ satisfies $\Gamma ^{\prime}(\theta )=\sqrt{I(\theta )}$ and $I(\theta )$ is the Fisher information corresponding to the density $p(x,\theta ).$