  We consider removing lower order statistics from the classical Hill estimator
in extreme value statistics, and compensating for it by rescaling the remaining
terms. Trajectories of these trimmed statistics as a function of the extent of
trimming turn out to be quite flat near the optimal threshold value. For the
regularly varying case, the classical threshold selection problem in tail
estimation is then revisited, both visually via trimmed Hill plots and, for the
Hall class, also mathematically via minimizing the expected empirical variance.
This leads to a simple threshold selection procedure for the classical Hill
estimator which circumvents the estimation of some of the tail characteristics,
a problem which is usually the bottleneck in threshold selection. As a
by-product, we derive an alternative estimator of the tail index, which assigns
more weight to large observations, and works particularly well for relatively
lighter tails. A simple ratio statistic routine is suggested to evaluate the
goodness of the implied selection of the threshold. We illustrate the
favourable performance and the potential of the proposed method with simulation
studies and real insurance data.
