Outliers in a time series often cause problems in fitting a suitable model to the data. Hence predictions based on such models are liable to be erroneous. In this paper we consider a stable first-order autoregressive process and suggest two methods of substituting an outlier by imputed values and then predicting on the basis of it. The asymptotic properties of both the process parameter estimators and the predictors are also studied.
@article{bwmeta1.element.bwnjournal-article-doi-10_4064-am33-3-2,
author = {Sugata Sen Roy and Sourav Chakraborty},
title = {Prediction problems related to a first-order autoregressive process in the presence of outliers},
journal = {Applicationes Mathematicae},
volume = {33},
year = {2006},
pages = {265-274},
zbl = {1105.62088},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-doi-10_4064-am33-3-2}
}
Sugata Sen Roy; Sourav Chakraborty. Prediction problems related to a first-order autoregressive process in the presence of outliers. Applicationes Mathematicae, Tome 33 (2006) pp. 265-274. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_4064-am33-3-2/