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http://dx.doi.org/10.5351/KJAS.2006.19.2.305

Outlier Detection of Autoregressive Models Using Robust Regression Estimators  

Lee Dong-Hee (Institute of Statistics, Korea University)
Park You-Sung (Dept. of Statistics, Korea University)
Kim Kee-Whan (Dept. of Informational Statistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.19, no.2, 2006 , pp. 305-317 More about this Journal
Abstract
Outliers adversely affect model identification, parameter estimation, and forecast in time series data. In particular, when outliers consist of a patch of additive outliers, the current outlier detection procedures suffer from the masking and swamping effects which make them inefficient. In this paper, we propose new outlier detection procedure based on high breakdown estimators, called as the dual robust filtering. Empirical and simulation studies in the autoregressive model with orders p show that the proposed procedure is effective.
Keywords
additive outlier; autoregressive model; high breakdown estimator; innovative outlier; outlying patch; robust filter; time reversibility;
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