Asymptotics of nonparametric L-1 regression models with dependent data
Abstract
We investigate asymptotic properties of least-absolute-deviation or median quantile estimates of the location and scale functions in nonparametric regression models with dependent data from multiple subjects. Under a general dependence structure that allows for longitudinal data and some spatially correlated data, we establish uniform Bahadur representations for the proposed median quantile estimates. The obtained Bahadur representations provide deep insights into the asymptotic behavior of the estimates. Our main theoretical development is based on studying the modulus of continuity of kernel weighted empirical process through a coupling argument. Progesterone data is used for an illustration.
Cite
@article{arxiv.1407.1225,
title = {Asymptotics of nonparametric L-1 regression models with dependent data},
author = {Zhibiao Zhao and Ying Wei and Dennis K. J. Lin},
journal= {arXiv preprint arXiv:1407.1225},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.3150/13-BEJ532 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)