  Consider a random vector (X, T), where X is d-dimensional and T is
one-dimensional. We suppose that the random variable T is subject to random
right censoring and satisfies the $\alpha$-mixing property. The aim of this
paper is to study the behavior of the kernel estimator of the relative error
regression and to establish its uniform almost sure consistency with rate.
Furthermore, we have highlighted the covariance term which measures the
dependency. The simulation study shows that the proposed estimator performs
well for a finite sample size in different cases.
