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Evolutionary Computation for Real-World Problems Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Recent Results on Nonparametric Quantile Estimation in a Simulation Model

Author : Adam Krzyżak

Published in: Challenges in Computational Statistics and Data Mining

Publisher: Springer International Publishing

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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.

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previous chapter Dependence Factor as a Rule Evaluation Measure
next chapter Adaptive Monte Carlo Maximum Likelihood
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Metadata
Title
Recent Results on Nonparametric Quantile Estimation in a Simulation Model
Author
Adam Krzyżak
Copyright Year
2016
Book
Challenges in Computational Statistics and Data Mining

Print ISBN: 978-3-319-18780-8

Electronic ISBN: 978-3-319-18781-5

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-18781-5_13

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