Maximum likelihood drift estimation for Gaussian process with stationary increments

Abstract

The paper deals with the regression model $X_t=\theta t+B_t$, $t\in [0,T]$, where $B=\{B_t,t\ge 0\}$ is a centered Gaussian process with stationary increments. We study the estimation of the unknown parameter $\theta$ and establish the formula for the likelihood function in terms of a solution to an integral equation. Then we find the maximum likelihood estimator and prove its strong consistency. The results obtained generalize the known results for fractional and mixed fractional Brownian motion.

12 pages

References in corpus (1)

• Construction of maximum likelihood estimator in the mixed fractional--fractional Brownian motion model with double long-range dependence

Cited by in corpus (1)

• Parameter estimation for Gaussian processes with application to the model with two independent fractional Brownian motions