Estimation of extreme depth-based quantile regions

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11 Citations (Scopus)


Consider the extreme quantile region induced by the half-space depth function HD of the form Q={x∈R^d ∶HD(x,P)≤β}, such that PQ = p for a given, very small p>0. Since this involves extrapolation outside the data cloud, this region can hardly be estimated through a fully non-parametric procedure. Using extreme value theory we construct a natural semiparametric estimator of this quantile region and prove a refined consistency result. A simulation study clearly demonstrates the good performance of our estimator. We use the procedure for risk management by applying it to stock market returns.
Original languageEnglish
Pages (from-to)449-461
JournalJournal of the Royal Statistical Society Series B-Statistical Methodology
Issue number2
Publication statusPublished - Mar 2017


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