Using a bootstrap method to choose the sample fraction in tail index estimation
| Autor: | Jon Danielsson, L. de Haan, Liang Peng, C.G. de Vries |
|---|---|
| Přispěvatelé: | Erasmus School of Economics |
| Rok vydání: | 2000 |
| Předmět: |
Statistics and Probability
Heteroscedasticity tail index bootstrap bias mean squared error optimal extreme sample fraction Numerical Analysis Mean squared error Stochastic process Estimation theory Order statistic Monte Carlo method bias bootstrap mean squared error optimal extreme sample fraction tail index Tail index Bootstrap Bias Statistics QA Mathematics Statistics Probability and Uncertainty Extreme value theory Student's t-test Mathematics |
| Zdroj: | Journal of Multivariate Analysis, 76(2), 226-248. Academic Press |
| ISSN: | 0047-259X |
| Popis: | Tail index estimation depends for its accuracy on a precise choice of the sample fraction, i.e., the number of extreme order statistics on which the estimation is based. A complete solution to the sample fraction selection is given by means of a two-step subsample bootstrap method. This method adaptively determines the sample fraction that minimizes the asymptotic mean-squared error. Unlike previous methods, prior knowledge of the second-order parameter is not required. In addition, we are able to dispense with the need for a prior estimate of the tail index which already converges roughly at the optimal rate. The only arbitrary choice of parameters is the number of Monte Carlo replications. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect