期刊
JOURNAL OF FORECASTING
卷 20, 期 8, 页码 565-579出版社
JOHN WILEY & SONS LTD
DOI: 10.1002/for.768
关键词
time series; additive outlier; innovation outlier; level shifts
A diagnostic procedure for detecting additive and innovation outliers as well as level shifts in a regression model with ARIMA errors is introduced. The procedure is based on a robust estimate of the model parameters and on innovation residuals computed by means of robust filtering. A Monte Carlo study shows that, when there is a large proportion of outliers, this procedure is more powerful than the classical methods based on maximum likelihood type estimates and Kalman filtering. Copyright (C) 2001 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据