Parameter estimation for random sampled Regression Model with Long Memory Noise
Statistics Theory
2019-02-25 v1 Methodology
Statistics Theory
Abstract
In this article, we present the least squares estimator for the drift parameter in a linear regression model driven by the increment of a fractional Brownian motion sampled at random times. For two different random times, Jittered and renewal process sampling, consistency of the estimator is proven. A simulation study is provided to illustrate the performance of the estimator under different values of the Hurst parameter H.
Cite
@article{arxiv.1902.08590,
title = {Parameter estimation for random sampled Regression Model with Long Memory Noise},
author = {Héctor Araya and Natalia Bahamonde and Lisandro Fermín and Tania Roa and Soledad Torres},
journal= {arXiv preprint arXiv:1902.08590},
year = {2019}
}
Comments
19 pages, 4 figures