Extreme Value Theory Filtering Techniques for Outlier Detection
AbstractWe introduce asymptotic parameter-free hypothesis tests based on extreme value theory to detect outlying observations in finite samples. Our tests have nontrivial power for detecting outliers for general forms of the parent distribution and can be implemented when this is unknown and needs to be estimated. Using these techniques this article also develops an algorithm to uncover outliers masked by the presence of influential observations.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Department of Economics, City University London in its series Working Papers with number 09/09.
Date of creation: 2009
Date of revision:
Contact details of provider:
Postal: Department of Economics, Social Sciences Building, City University London, Whiskin Street, London, EC1R 0JD, United Kingdom,
Phone: +44 (0)20 7040 8500
Web page: http://www.city.ac.uk
More information through EDIRC
Extreme value theory; Hypothesis tests; Outlier detection; Power function; Robust estimation;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jose Olmo & Jesus Gonzalo, 2004.
"Which Extreme Values are Really Extremes?,"
Econometric Society 2004 North American Winter Meetings
144, Econometric Society.
- Armelle Guillou & Peter Hall, 2001. "A diagnostic for selecting the threshold in extreme value analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 293-305.
- Basmann, Robert L., 2003. "Statistical outlier analysis in litigation support: the case of Paul F. Engler and Cactus Feeders, Inc., v. Oprah Winfrey et al," Journal of Econometrics, Elsevier, vol. 113(1), pages 159-200, March.
- Schluter, Christian & Trede, Mark, 2008. "Identifying multiple outliers in heavy-tailed distributions with an application to market crashes," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 700-713, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Research Publications Librarian).
If references are entirely missing, you can add them using this form.