In this paper we discuss a preliminary results on the construction of a weighted likelihood procedure for robust estimation of the unknown parameters of an autoregressive-moving average model. Two types of outliers, i.e., additive and innovations be take into account without knowing their number, position or intensity. A classification procedure based on a selection criterion is used to identified the most useful pattern rappresentation of the outliers to gain efficiency and robustness. Two examples are reported.

Robust time series estimation via weighted likelihood. Some preliminary results.

Agostinelli, Claudio
2001

Abstract

In this paper we discuss a preliminary results on the construction of a weighted likelihood procedure for robust estimation of the unknown parameters of an autoregressive-moving average model. Two types of outliers, i.e., additive and innovations be take into account without knowing their number, position or intensity. A classification procedure based on a selection criterion is used to identified the most useful pattern rappresentation of the outliers to gain efficiency and robustness. Two examples are reported.
File in questo prodotto:
File Dimensione Formato  
35_pdfsam_WP_2001_parte_1.pdf

accesso aperto

Licenza: Non specificato
Dimensione 30.22 MB
Formato Adobe PDF
30.22 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3442463
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact