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Recent Results on Nonparametric Quantile Estimation in a Simulation Model

  • Adam KrzyżakEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 605)

Abstract

We present recent results on nonparametric estimation of a quantile of distribution of Y given by a simulation model \(Y=m(X)\), where \(m: \mathbb {R}^d\rightarrow \mathbb {R}\) is a function which is costly to compute and X is a \(\mathbb {R}^d\)-valued random variable with given density. We argue that importance sampling quantile estimate of m(X), based on a suitable estimate \(m_n\) of m achieves better rate of convergence than the estimate based on order statistics alone. Similar results are given for Robbins-Monro type recursive importance sampling and for quantile estimation based on surrogate model.

Keywords

Order Statistic Importance Sampling Surrogate Function Piecewise Polynomial Monte Carlo Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The author would like to thank his co-authors and acknowledge the support from the Natural Sciences and Engineering Research Council of Canada.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Software EngineeringConcordia UniversityMontrealCanada

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