Conditional quantile sequential estimation for stochastic codes
Statistics Theory
2019-08-06 v6 Statistics Theory
Abstract
We propose and analyze an algorithm for the sequential estimation of a conditional quantile in the context of real stochastic codes with vectorvalued inputs. Our algorithm is based on k-nearest neighbors smoothing within a Robbins-Monro estimator. We discuss the convergence of the algorithm under some conditions on the stochastic code. We provide non-asymptotic rates of convergence of the mean squared error and we discuss the tuning of the algorithm's parameters.
Cite
@article{arxiv.1508.06505,
title = {Conditional quantile sequential estimation for stochastic codes},
author = {Tatiana Labopin-Richard and Fabrice Gamboa and Aurélien Garivier and Jerome Stenger},
journal= {arXiv preprint arXiv:1508.06505},
year = {2019}
}