# A simple approach to construct confidence bands for a regression function with incomplete data

## TL;DR

This paper introduces a new kernel-based method for constructing asymptotically valid confidence bands for regression functions with incomplete data, addressing challenges of unknown data distribution and missing responses.

## Contribution

It proposes a novel kernel-type estimator and derives its limiting distribution to enable accurate confidence band construction under missing data conditions.

## Key findings

- The method achieves asymptotically correct coverage probabilities.
- Numerical studies show good finite-sample performance.
- The approach handles unknown density and variance functions.

## Abstract

A long-standing problem in the construction of asymptotically correct confidence bands for a regression function $m(x)=E[Y|X=x]$, where $Y$ is the response variable influenced by the covariate $X$, involves the situation where $Y$ values may be missing at random, and where the selection probability, the density function $f(x)$ of $X$, and the conditional variance of $Y$ given $X$ are all completely unknown. This can be particularly more complicated in nonparametric situations. In this paper we propose a new kernel-type regression estimator and study the limiting distribution of the properly normalized versions of the maximal deviation of the proposed estimator from the true regression curve. The resulting limiting distribution will be used to construct uniform confidence bands for the underlying regression curve with asymptotically correct coverages. The focus of the current paper is on the case where $X\in \mathbb{R}$. We also perform numerical studies to assess the finite-sample performance of the proposed method. In this paper, both mechanics and the theoretical validity of our methods are discussed.