Parameter estimation for random sampled Regression Model with Long Memory Noise

02/22/2019

∙

by   Héctor Araya, et al.

∙

0

∙

share

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.