Influential Observations in Time Series
研究如何在单变量自回归移动平均时间序列模型中识别有影响力的观测值，并衡量它们对模型参数估计的影响，使用基于马氏距离的统计量来检测加性异常值和创新异常值。
This article studies how to identify influential observations in univariate autoregressive integrated moving average time series models and how to measure their effects on the estimated parameters of the model. The sensitivity of the parameters to the presence of either additive or innovational outliers is analyzed, and influence statistics based on the Mahalanobis distance are presented. The statistic linked to additive outliers is shown to be very useful for indicating the robustness of the fitted model to the given data set. Its application is illustrated using a relevant set of historical data.