Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood-based method in finite samples and indicates that the proposed methods are preferable when dealing with the non-Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood.
Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood-based method in finite samples and indicates that the proposed methods are preferable when dealing with the non-Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood.
Empirical likelihood for outlier detection and estimation in autoregressive time series / Baragona, Roberto; Battaglia, Francesco; Cucina, Domenico. - In: JOURNAL OF TIME SERIES ANALYSIS. - ISSN 1467-9892. - STAMPA. - 37:3(2016), pp. 315-336. [10.1111/jtsa.12145]
Empirical likelihood for outlier detection and estimation in autoregressive time series
BARAGONA, Roberto;BATTAGLIA, Francesco;CUCINA, Domenico
2016
Abstract
Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood-based method in finite samples and indicates that the proposed methods are preferable when dealing with the non-Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood.File | Dimensione | Formato | |
---|---|---|---|
Battaglia_.Empirical-likelihood_2016.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
329.36 kB
Formato
Adobe PDF
|
329.36 kB | Adobe PDF | Contatta l'autore |
paperELRtimeseriesR1.pdf
solo utenti autorizzati
Note: pre-print
Tipologia:
Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
165.66 kB
Formato
Adobe PDF
|
165.66 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.