  The distributed Hill estimator is a divide-and-conquer algorithm for
estimating the extreme value index when data are stored in multiple machines.
In applications, estimates based on the distributed Hill estimator can be
sensitive to the choice of the number of the exceedance ratios used in each
machine. Even when choosing the number at a low level, a high asymptotic bias
may arise. We overcome this potential drawback by designing a bias correction
procedure for the distributed Hill estimator, which adheres to the setup of
distributed inference. The asymptotically unbiased distributed estimator we
obtained, on the one hand, is applicable to distributed stored data, on the
other hand, inherits all known advantages of bias correction methods in extreme
value statistics.
