Nonparametric M-estimation for right censored regression model with stationary ergodic data
Methodology
2016-05-03 v1
Abstract
The present paper deals with a nonparametric M-estimation for right censored regression model with stationary ergodic data. Defined as an implicit function, a kernel type estimator of a family of robust regression is considered when the covariate take its values in R^d (d >= 1) and the data are sampled from stationary ergodic process. The strong consistency (with rate) and the asymptotic distribution of the estimator are established under mild assumptions. Moreover, a usable confidence interval is provided which does not depend on any unknown quantity. Our results hold without any mixing condition and do not require the existence of marginal densities. A comparison study based on simulated data is also provided.
Cite
@article{arxiv.1605.00015,
title = {Nonparametric M-estimation for right censored regression model with stationary ergodic data},
author = {Mohamed Chaouch and Naamane Laib and Elias Ould-Said},
journal= {arXiv preprint arXiv:1605.00015},
year = {2016}
}