Calculating the Distribution of the Serial Correlation Estimator by Saddlepoint Integration
描述并应用数值鞍点积分方法，计算一阶自回归正态时间序列中相关系数的最大似然和Yule-Walker估计量的概率分布，适用于有限样本和不同初始条件，并展示有限样本分布如何随样本量增加趋近渐近分布。
The efficient method of numerical saddlepoint integration is described and applied to calculating the probability distribution of the maximum likelihood and Yule-Walker estimators of the correlation coefficient a of a first-order autoregressive normal time series with initial value either zero or nonzero when a finite number n of data are at hand. Stationary time series of the same type are also treated. Significance points are computed in a number of examples to show how, as n increases, the finite-sample distributions approach the asymptotic distributions that have appeared in the literature.