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- W2055584610 abstract "The presence of outliers can have a profound effect on model selection, parameter estimation and prediction for a wide range of models. We consider the problem of identifying the lag structure of a sub-set autoregression from data contaminated by additive outliers. We assume that the latter are generated by non-systematic changes to the system under study. We focus on backward selection algorithms, and describe a number of new procedures based on data cleaning (the process of replacing outliers by more moderate values) and robust model selection criteria. We evaluate our suggestions in a numerical experiment which shows that they give significant insurance against outliers. This is in stark contrast to conventional techniques which experience catastrophic reductions in performance in outlier contaminated data. While no robust procedure gave the best performance for all models, we note that techniques based on cleaning out performed criterion-based techniques in outlier contaminated data. However, all robust procedures generally experience reductions in performance in outlier free data." @default.
- W2055584610 created "2016-06-24" @default.
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- W2055584610 date "2001-04-01" @default.
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- W2055584610 title "Selecting sub-set autoregressions from outlier contaminated data" @default.
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- W2055584610 doi "https://doi.org/10.1016/s0167-9473(00)00038-4" @default.
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