Estimation and reduced bias estimation of the residual dependence index with unnamed marginals

Unlike univariate extreme value theory, multivariate extreme value distributions cannot be specified through a finite-dimensional parameter family of distributions. Instead, the many facets of multivariate extremes are mirrored in the inherent dependence structure of component-wise maxima which must be dissociated from the limiting extreme behaviour of its marginal distribution functions before a probabilistic characterisation of an extreme value quality can be determined. Mechanisms applied to elicit extremal dependence typically rely on standardisation of the unknown marginal distribution functions from which pseudo-observations for either Pareto or Fr\'echet marginals result. The relative merits of both of these choices for transformation of marginals have been discussed in the literature, particularly in the context of domains of attraction of an extreme value distribution. This paper is set within this context of modelling penultimate dependence as it proposes a unifying class of estimators for the residual dependence index that eschews consideration of choice of marginals. In addition, a reduced bias variant of the new class of estimators is introduced and their asymptotic properties are developed. The pivotal role of the unifying marginal transform in effectively removing bias is borne by a comprehensive simulation study. The leading application in this paper comprises an analysis of asymptotic independence between rainfall occurrences originating from monsoon-related events at several locations in Ghana.