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.
Outliers in functional auutoregressive time series / Battaglia, Francesco. - In: STATISTICS & PROBABILITY LETTERS. - ISSN 0167-7152. - 72(2005), pp. 323-332. [10.1016/j.spl.2005.02.003]
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Titolo: | Outliers in functional auutoregressive time series | |
Autori: | ||
Data di pubblicazione: | 2005 | |
Rivista: | ||
Citazione: | Outliers in functional auutoregressive time series / Battaglia, Francesco. - In: STATISTICS & PROBABILITY LETTERS. - ISSN 0167-7152. - 72(2005), pp. 323-332. [10.1016/j.spl.2005.02.003] | |
Handle: | http://hdl.handle.net/11573/36972 | |
Appartiene alla tipologia: | 01a Articolo in rivista |