Given an ordered sample of either exceedances or upper order statistics which is to be modeled using a GPD, this
function provides Falk's estimator of the shape parameter \(\gamma \in [-1,0]\). Precisely,
$$\hat \gamma_{\rm{Falk}} = \hat \gamma_{\rm{Falk}}(k, n) = \frac{1}{k-1} \sum_{j=2}^k \log \Bigl(\frac{X_{(n)}-H^{-1}((n-j+1)/n)}{X_{(n)}-H^{-1}((n-k)/n)} \Bigr), \; \; k=3, \ldots ,n-1$$
for $H$ either the empirical or the distribution function based on the log--concave density estimator.
Note that for any \(k\), \(\hat \gamma_{\rm{Falk}} : R^n \to (-\infty, 0)\). If
\(\hat \gamma_{\rm{Falk}} \not \in [-1,0)\), then it is likely that the log-concavity assumption is violated.