A bootstrap-based method to achieve optimality on estimating the extreme-value index
Estimators of the extreme-value index are based on a set of upper order statistics. We present an adaptive method to choose the number of order statistics involved in an optimal way, balancing variance and bias components. Recently this has been achieved for the similar but somewhat less involved case of regularly varying tails (Drees and Kaufmann(1997); Danielsson et al.(1996)). The present paper follows the line of proof of the last mentioned paper.
|Keywords||Pickands estimator, bootstrap, mean squared error, moment estimator|
Draisma, G., de Haan, L.F.M., Peng, L., & Pereira, T.T.. (2000). A bootstrap-based method to achieve optimality on estimating the extreme-value index (No. EI 2000-18/A). Retrieved from http://hdl.handle.net/1765/1650