Clustering of exceedances of a critical level is a phenomenon that concerns risk managers
in many areas. The extremal index θ measures the propensity of the large
observations in a dataset to cluster. Thus the estimation of θ is an important issue
recurrently addressed in literature. Besides a declustering parameter, inference also
depends on a threshold. This choice is actually a crucial topic and is transversal to
many other extremal parameters. In this paper we analyze a threshold-free heuristic
procedure. We also make comparisons with other heuristic procedures already
developed within the extremal index estimation. Our study is based on simulation.
We illustrate with an application to environmental data.This research was financed by Portuguese Funds through FCT — Fundação
para a Ciência e a Tecnologia, through the projects UID/MAT/00013/2013 and
UID/MAT/00006/2013 and by the Research Center CEMAT through the Project
UID/Multi/04621/2013.info:eu-repo/semantics/publishedVersio
