Confidence Intervals for Autocorrelations Based on Cyclic Samples
针对Clinger和Van Ness提出的周期性采样方法，推导了低阶自回归移动平均过程中自相关估计的近似置信区间，并用植物病害流行病学数据演示。
Abstract Clinger and Van Ness have shown how to sample a stochastic process in a periodic manner such that the autocorrelation function for that process can be estimated at all lags. We derive approximate confidence intervals for these estimates, focusing on low-order autoregressive moving average processes. Our methods are illustrated with an example from plant disease epidemiology.