This paper by Robert Fox and Murad S. Taqqu discusses the large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series. The authors propose an estimator for the unknown parameters $\theta$ and show that it is consistent and asymptotically normal under appropriate conditions. These conditions are satisfied by fractional Gaussian noise and fractional ARMA processes, which are examples of strongly dependent sequences. The paper provides detailed mathematical proofs and conditions for the consistency and asymptotic normality of the estimator, including Lemmas and Theorems that establish the theoretical foundations. The results are applicable to fractional Gaussian noise and fractional ARMA processes, which are used to model strongly dependent phenomena in various fields such as geophysics and hydrology.This paper by Robert Fox and Murad S. Taqqu discusses the large-sample properties of parameter estimates for strongly dependent stationary Gaussian time series. The authors propose an estimator for the unknown parameters $\theta$ and show that it is consistent and asymptotically normal under appropriate conditions. These conditions are satisfied by fractional Gaussian noise and fractional ARMA processes, which are examples of strongly dependent sequences. The paper provides detailed mathematical proofs and conditions for the consistency and asymptotic normality of the estimator, including Lemmas and Theorems that establish the theoretical foundations. The results are applicable to fractional Gaussian noise and fractional ARMA processes, which are used to model strongly dependent phenomena in various fields such as geophysics and hydrology.