Estimation of Time Series Parameters in the Presence of Outliers
作者:
Ih Chang,
GeorgeC. Tiao,
Chung Chen,
期刊:
Technometrics
(Taylor Available online 1988)
卷期:
Volume 30,
issue 2
页码: 193-204
ISSN:0040-1706
年代: 1988
DOI:10.1080/00401706.1988.10488367
出版商: Taylor & Francis Group
关键词: Additive outlier;Innovational outlier;ARIMA model;Intervention;Robust estimate
数据来源: Taylor
摘要:
Outliers in time series can be regarded as being generated by dynamic intervention models at unknown time points. Two special cases, innovational outlier (IO) and additive outlier (AO), are studied in this article. The likelihood ratio criteria for testing the existence of outliers of both types, and the criteria for distinguishing between them are derived. An iterative procedure is proposed for detecting IO and AO in practice and for estimating the time series parameters in autoregressive-integrated-moving-average models in the presence of outliers. The powers of the procedure in detecting outliers are investigated by simulation experiments. The performance of the proposed procedure for estimating the autoregressive coefficient of a simple AR(l) model compares favorably with robust estimation procedures proposed in the literature. Two real examples are presented.
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