Nonparametric recursive quantile estimation

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<p≤d the rate of convergence log3+p/2(n)⋅n−1/2−p/(2d).

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Year of publication:	2014
Authors:	Kohler, Michael ; Krzyżak, Adam ; Walk, Harro
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 93.2014, C, p. 102-107
Publisher:	Elsevier
Subject:	Nonparametric quantile estimation \| Importance sampling \| Rate of convergence \| Robbins–Monro procedure

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011039780