  This article is devoted to the study of tail index estimation based on i.i.d.
multivariate observations, drawn from a standard heavy-tailed distribution,
i.e. of which 1-d Pareto-like marginals share the same tail index. A
multivariate Central Limit Theorem for a random vector, whose components
correspond to (possibly dependent) Hill estimators of the common shape index
alpha, is established under mild conditions. Motivated by the statistical
analysis of extremal spatial data in particular, we introduce the concept of
(standard) heavy-tailed random field of tail index alpha and show how this
limit result can be used in order to build an estimator of alpha with small
asymptotic mean squared error, through a proper convex linear combination of
the coordinates. Beyond asymptotic results, simulation experiments illustrating
the relevance of the approach promoted are also presented.
