Detection of outlier patches in autoregressive time series

A. Justel and Ruey S. Tsay
Authors registered in the RePEc Author Service: Daniel Peña

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: This paper proposed a procedure to identify patches of outliers in an autoregressive process. The procedure is an improvement over the existing outlier detection methods via Gibbs sampling. It identifies the beginning and end of possible outlier patches using the existing Gibbs sampling, then carries out and adaptive procedure with block interpolation to handle patches of outliers. Empirical and simulated examples show that the proposed procedure is effective in handling masking and swamping effects caused by multiple outliers. The real example also shows that the standard Gibbs sampling to outlier detection may encounter severe masking and swamping effects in practice.

Keywords: Multiple; outliers; Sequential; learning; Gibbs; sampler; Time; series (search for similar items in EconPapers)
Date: 1998-02
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://e-archivo.uc3m.es/rest/api/core/bitstreams ... a717f071c144/content (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:9821

Access Statistics for this paper

More papers in DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística
Bibliographic data for series maintained by Ana Poveda ().