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EnglishA method for identifying and estimating outliers in a time series is proposed, based on fitting functional autoregressive models. Both additive and innovation outliers may be defined. A simulation experiment and the analysis of some real data sets suggest that the proposed method is effective both for series following some nonlinear models, such as self-exciting threshold autoregressive or exponential autoregressive, and for linear series generated by autoregressive moving average processes.
http://hdl.handle.net/11573/36972| Titolo: | Outliers in functional auutoregressive time series |
| Autori interni: | BATTAGLIA, Francesco |
| Data di pubblicazione: | 2005 |
| Rivista: | STATISTICS & PROBABILITY LETTERS |
| Abstract: | A method for identifying and estimating outliers in a time series is proposed, based on fitting functional autoregressive models. Both additive and innovation outliers may be defined. A simulation experiment and the analysis of some real data sets suggest that the proposed method is effective both for series following some nonlinear models, such as self-exciting threshold autoregressive or exponential autoregressive, and for linear series generated by autoregressive moving average processes. |
| Handle: | http://hdl.handle.net/11573/36972 |
| Appare nelle tipologie: | 01.a Pubblicazione su Rivista |
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