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.
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