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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 indicesof stability, $DS(\alpha_1, \ldots, \alpha_p)$, such that $1<\alpha_{i}\leq 2$,$i=1,\ldots,p$. The suggested estimator is asymptotically Gaussian with unknownparameters. We apply an asymptotically valid bootstrap to construct aconfidence region for the mean vector. A simulation study is performed to showthat the estimation method is efficient for conducting inference about the meanvector for multivariate heavy-tailed distributions.

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