On Goodness of Fit of Time Series Models: An Application of Higher Order Crossings
讨论了平稳时间序列图形与自相关的关系，提出用高阶交叉计数检验模型拟合优度，并与Box-Pierce Q统计量在真实和模拟数据上比较。
A relation between the graphical appearance of a stationary time series and its autocorrelations is discussed. The use of counts of visual features termed higher order crossings is proposed in testing goodness of fit of hypothesized models against very general alternatives. Attention is focused on a specific example of a new type of nonparametric test statistic arising from iterative feature extraction procedures called extractors. The statistic is compared with Box & Pierce's portmanteau Q statistic on real data as well as on simulated normal autoregressive-moving average series.