Extreme Value Theory Filtering Techniques for Outlier Detection
We 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.
|Date of creation:||2009|
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- Jesus Gonzalo, 2004.
"Which Extreme Values Are Really Extreme?,"
Journal of Financial Econometrics,
Society for Financial Econometrics, vol. 2(3), pages 349-369.
- Jose Olmo & Jesus Gonzalo, 2004. "Which Extreme Values are Really Extremes?," Econometric Society 2004 North American Winter Meetings 144, Econometric Society.
- 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.
- 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.
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