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Time series data are often subject to uncontrolled or
unexpected interventions, from which various types of outlying
observations or structure changes are produced. In this
article, we focus on detecting and treating structure change
events in multiple time series by studying transfer function
models with one input series. Monte Carlo simulations will be
used to study the performance of the proposed procedures.
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