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Nonparametric quantile estimation using surrogate models and importance sampling

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Abstract

Given a costly to compute function \(m: {\mathbb {R}}^d\rightarrow {\mathbb {R}}\), which is part of a simulation model, and an \({\mathbb {R}}^d\)-valued random variable with known distribution, the problem of estimating a quantile \(q_{m(X),\alpha }\) is investigated. The presented approach has a nonparametric nature. Monte Carlo quantile estimates are obtained by estimating m through some estimate (surrogate) \(m_n\) and then by using an initial quantile estimate together with importance sampling to construct an importance sampling surrogate quantile estimate. A general error bound on the error of this quantile estimate is derived, which depends on the local error of the function estimate \(m_n\), and the convergence rates of the corresponding importance sampling surrogate quantile estimates are analyzed. The finite sample size behavior of the estimates is investigated by applying the estimates to simulated data.

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Acknowledgements

The authors would like to thank the German Research Foundation (DFG) for funding this project within the Collaborative Research Centre 805 (Projektnummer 57157408 - SFB 805). Furthermore the authors would like to thank two anonymous referees and the Associate editor for many very useful comments which improved an early version of this article.

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  1. Fachbereich Mathematik, Technische Universität Darmstadt, Schlossgartenstr. 7, 64289, Darmstadt, Germany

    Michael Kohler & Reinhard Tent

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  1. Michael Kohler
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  2. Reinhard Tent
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Correspondence to Michael Kohler.

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Kohler, M., Tent, R. Nonparametric quantile estimation using surrogate models and importance sampling. Metrika 83, 141–169 (2020). https://doi.org/10.1007/s00184-019-00736-3

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  • DOI: https://doi.org/10.1007/s00184-019-00736-3

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