  In extreme value analysis, sensitivity of inference to the definition of
extreme event is a paramount issue. Under the peaks-over-threshold (POT)
approach, this translates directly into the need of fitting a Generalized
Pareto distribution to observations above a suitable level that balances bias
versus variance of estimates. Selection methodologies established in the
literature face recurrent challenges such as an inherent subjectivity or high
computational intensity. We suggest a truly automated method for threshold
detection, aiming at time efficiency and elimination of subjective judgment.
Based on the well-established theory of L-moments, this versatile data-driven
technique can handle batch processing of large collections of extremes data,
while also presenting good performance on small samples.
  The technique's performance is evaluated in a large simulation study and
illustrated with significant wave height data sets from the literature. We find
that it compares favorably to other state-of-the-art methods regarding the
choice of threshold, associated parameter estimation and the ultimate goal of
computationally efficient return level estimation.
