We consider the class of estimators of the extreme value index [~ that can be represented as a scale invariant functional T applied to the empirical tail quantile function Q,. From an approximation of Q,, first asymptotic normality of T(Q~) is derived under quite natural smoothness conditions on 7"
Asymptotically unbiased estimators for the extreme-value index
โ Scribed by L. Peng
- Publisher
- Elsevier Science
- Year
- 1998
- Tongue
- English
- Weight
- 327 KB
- Volume
- 38
- Category
- Article
- ISSN
- 0167-7152
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โฆ Synopsis
Estimators of the extreme-value index are based on a set of upper order statistics. When the number of upper-order statistics used in the estimation of the extreme-value index is small, the variance of the estimator will be large. On the other hand, the use of a large number of upper statistics will introduce a big bias. There are several papers concerning how to balance the variance component and the bias component. In this paper, we give an unbiased estimator even if one uses a large number of upper-order statistics. (~
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