Autoregressive and Window Estimates of the Inverse Correlation Function
推导了平稳过程逆相关函数的自回归和窗估计方法的渐近分布，并证明Durbin提出的移动平均模型参数估计方法在高斯情形下渐近有效。
The concept of the inverse correlation function of a stationary process xt was first introduced by Cleveland (1972), who also introduced the autoregressive and the window methods for estimating this function. The asymptotic distribution of the estimates provided by these two methods is derived and their asymptotic covariance structure is shown to be in accordance with a remark of Parzen (1974). The results are extended to show that the two procedures suggested by Durbin (1959, 1961) for estimating the parameters of a moving average model are asymptotically efficient, relative to maximum likelihood in the Gaussian case.