@article {1971981,
	title = {Robustness of Tail Index Estimation},
	journal = {Journal of Computational and Graphical Statistics},
	volume = {8},
	year = {1999},
	month = {1999},
	pages = {318-332},
	abstract = {The 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.},
	keywords = {Supply Chain},
	author = {Hsieh,Ping-Hung}
}