01102nas a2200145   4500008004100000245004000041210004000081260000900121300001200130490000600142520069100148653001700839100002100856856007900877       1999                        eng d00aRobustness of Tail Index Estimation0 aRobustness of Tail Index Estimation  c1999  a318-3320 v83 aThe implementation of the Hill estimator, which estimates the heaviness of the tail of a distribution, requires a choice of the number of extreme observations in the tails, $r$, from a sample of size $n$, where $2 \leq r+1 \leq n$. This article is concerned with a robust procedure of choosing an optimal $r$. Thus, an estimation procedure, $\delta_s$, based on the idea of spacing statistics, $H^{(r)}$, is developed. The proposed decision rule for choosing $r$ under the squared error loss is found to be a simple function of the sample size. The proposed rule is then illustrated across a wide range of data, including insurance claims, currency exchange rate returns, and city size.10aSupply Chain1 aHsieh, Ping-Hung  u/biblio/robustness-tail-index-estimation-0