Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I(2) statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the ...

For a sequence of independent and identically distributed random vectorsXi = (X1 i , X2 i , . . . , X i ), i = 1, 2, . . . , n, we consider the conditional ordering of these random vectors with respect to the magnitudes of N(Xi ), i = 1, 2, . . . , n, where N is a p-variate continuous function defined on the support set of X1 and satisfying certain regularity conditions. We also consider the Pr...

As in other areas of geophysics or meteorology, the observations and data collected at volcanoes are the result of experiments in which we cannot control the variables we wish to study. Thus, statistical analysis is an extremely important step in the data processing. Variations in the experimental parameters must be controlled through the choice of samples and through the hypotheses chosen for ...

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Extending umbral methods introduced by Di Nardo and Senato (2006b), in this paper we provide an unifying syntax for single and multivariate k-statistics, polykays and multivariate polykays. From a combinatorial point of view, we revisit the theory as exposed by Stuart and Ord (1987) taking into account the Doubilet approach to symmetric functions. The Moebius function, occurring in the relation...

In this review paper we outline some recent contributions to copula theory. Several new author's investigations are presented brie°y, namely: order statistics copula, copulas with given multivariate marginals, copula representation via a local dependence measure and applications of extreme value copulas. Key-words: Copula; Dependence measures; Extremes; Kendall distribution; Local dependence; M...

Abstract The multivariate normal and the multivariate t distributions belong to the most widely used multivariate distributions in statistics, quantitative risk management, and insurance. In contrast to the multivariate normal distribution, the parameterization of the multivariate t distribution does not correspond to its moments. This, paired with a non-standard implementation in the R package...

We propose a multivariate extreme value threshold model for joint tail estimation which overcomes the problems encountered with existing techniques when the variables are near independence. We examine inference under the model and develop tests for independence of extremes of the marginal variables, both when the thresholds are fixed, and when they increase with the sample size. Motivated by re...

Diffusion tensor imaging (DTI) is important for characterizing the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. There has been extensive interest in the analysis of diffusion properties measured along fiber tracts as a function of age, diagnostic status, and gender, while controlling for other clinical variables. However, the e...