Aproximate Distributions of the Periodogram and Related Statistics under Normality
在正态性假设下，利用周期图可分解为两个独立随机变量之和的定理，推导了周期图及其衍生统计量（如谱估计和预测误差方差估计）的高阶近似分布，并通过模拟图形展示近似效果。
Under normality, we obtain higher-order approximations to the distributions of the periodogram and related statistics. Our approach is based on the theorem which decomposes the periodogram into the sum of two independent random variables. It is seen that this decomposition enables us to study fairly closely the higher-order properties of not only the periodogram, but also periodogram-based statistics such as the estimators of the spectrum and prediction error variance. Some of the approximation results are graphically presented together with the exact results based on simulations.