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Nonparametric recursive quantile estimation

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  • Kohler, Michael
  • Krzyżak, Adam
  • Walk, Harro

Abstract

A simulation model with outcome Y=m(X) is considered, where X is an Rd-valued random variable and m:Rd→R is p-times continuously differentiable. It is shown that an importance sampling Robbins–Monro type quantile estimate achieves for 0

Suggested Citation

  • Kohler, Michael & Krzyżak, Adam & Walk, Harro, 2014. "Nonparametric recursive quantile estimation," Statistics & Probability Letters, Elsevier, vol. 93(C), pages 102-107.
    • Handle: RePEc:eee:stapro:v:93:y:2014:i:c:p:102-107
    DOI: 10.1016/j.spl.2014.06.007
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    References listed on IDEAS

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    1. Kohler, Michael & Krzyżak, Adam, 2013. "Optimal global rates of convergence for interpolation problems with random design," Statistics & Probability Letters, Elsevier, vol. 83(8), pages 1871-1879.
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    Cited by:

    1. Michael Kohler & Adam Krzyżak & Reinhard Tent & Harro Walk, 2018. "Nonparametric quantile estimation using importance sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(2), pages 439-465, April.
    2. Qiyun Pan & Eunshin Byon & Young Myoung Ko & Henry Lam, 2020. "Adaptive importance sampling for extreme quantile estimation with stochastic black box computer models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(7), pages 524-547, October.

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