 A class of distribution functions with less bias in extreme value estimationLaurens de Haan and Luisa Canto e Castro Statistics & Probability Letters, 2006, vol. 76, issue 15, 1617-1624 Abstract: Let X1,X2,... be i.i.d. random variables and let their distribution be in the domain of attraction of an extreme value distribution. Quite a few estimators of the extreme value index are known to be consistent under the domain of attraction conditions. When it comes to asymptotic normality a condition that is called second-order condition is very useful. The condition yields a speed of convergence of a polynomial rate. Then one gets asymptotically a normal distribution without bias, provided one restricts the number of tail observations used in the estimation to a certain polynomial of n, the total number of observations. We investigate what happens if the speed of convergence is faster than any polynomial rate. In that case one can use many more tail observations without creating bias. Keywords: Second-order; condition; Tail; quantile; process; Extreme; value; index; estimation (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:76:y:2006:i:15:p:1617-1624 Ordering information: This journal article can be ordered from Access Statistics for this article Statistics & Probability Letters is currently edited by Somnath Datta and Hira L. Koul More articles in Statistics & Probability Letters from Elsevier |
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