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 character-
istics, 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 good-
ness 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.