Kernel regression uniform rate estimation for censored data under $\alpha$-mixing condition

TL;DR

This paper investigates the uniform rate of convergence for kernel regression estimators in right censored data with $\alpha$-mixing dependence, establishing consistency and demonstrating through simulations.

Contribution

It introduces a uniform strong consistency result and convergence rate for kernel regression estimators under $\alpha$-mixing conditions in censored data models.

Findings

Establishes uniform strong consistency of the estimator.

Provides convergence rate under $\alpha$-mixing dependence.

Simulation results illustrate estimator behavior for finite samples.

Abstract

In this paper, we study the behavior of a kernel estimator of the regression function in the right censored model with $\alpha$-mixing data . The uniform strong consistency over a real compact set of the estimate is established along with a rate of convergence. Some simulations are carried out to illustrate the behavior of the estimate with different examples for finite sample sizes.