Estimating the Inverse Autocorrelation Function from Outlier Contaminated Data

R. H. Glendinning
Additional contact information
R. H. Glendinning: Defence Research Agency

Computational Statistics, 2000, vol. 15, issue 4, No 6, 565 pages

Abstract: Summary We show how a number of commonly used estimates of the inverse autocorrelation function can be modified to deal with outlier contaminated data. The robust analogues of the orthogonal and interpolation based techniques appear to be new, and provide an alternative to the robust autoregressive approach. We examine the performance of these techniques in a large scale numerical experiment. This shows significant improvements in performance in outlier contaminated data when robust techniques are used. While there was no uniformly best robust technique, our experiments support the use of the autoregressive approach to avoid catastrophic reductions in performance, and robust interpolation for short series corrupted by few outliers.

Keywords: Inverse autocorrelation; robust estimation; additive outlier; order selection (search for similar items in EconPapers)
Date: 2000
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s001800000048 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:compst:v:15:y:2000:i:4:d:10.1007_s001800000048

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s001800000048

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().