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Bootstrapping the Mean Vector for the Observations in the Domain of Attraction of a Multivariate Stable Law

We consider a robust estimation of the mean vector for a sequence of i.i.d. observations in the domain of attraction of a stable law with different indices of stability, $DS(α_1, \ldots, α_p)$, such that $1<α_{i}\leq 2$, $i=1,\ldots,p$. The suggested estimator is asymptotically Gaussian with unknown parameters. We apply an asymptotically valid bootstrap to construct a confidence region for the mean vector. A simulation study is performed to show that the estimation method is efficient for conducting inference about the mean vector for multivariate heavy-tailed distributions.