论文标题
由长记忆高斯噪声驱动的AR（1）模型的AR（1）模型的矩估计器
Moment estimator for an AR(1) model with non-zero mean driven by a long memory Gaussian noise
论文作者
论文摘要
在本文中，我们考虑了一阶自回归过程的推理问题，其均值均值均值为较长的记忆固定的高斯过程驱动。假设噪声的协方差函数可以表示为$ | k |^{2H-2} $ times $ times a当$ k $倾向于无穷大，而小数高斯噪声和分数arima模型是满足此假设的特殊示例。我们提出了力矩估计器，并证明了强大的一致性，渐近正态性和联合渐近态性。
In this paper, we consider an inference problem for the first order autoregressive process with non-zero mean driven by a long memory stationary Gaussian process. Suppose that the covariance function of the noise can be expressed as $|k|^{2H-2}$ times a positive constant when $k$ tends to infinity, and the fractional Gaussian noise and the fractional ARIMA model are special examples that satisfy this assumption. We propose moment estimators and prove the strong consistency, the asymptotic normality and joint asymptotic normality.
