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Conditional quantile sequential estimation for stochastic codes

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.
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Dates and versions

hal-01187329 , version 1 (26-08-2015)
hal-01187329 , version 2 (16-09-2015)
hal-01187329 , version 3 (11-12-2015)
hal-01187329 , version 4 (28-01-2016)
hal-01187329 , version 5 (19-05-2016)
hal-01187329 , version 6 (13-05-2019)
hal-01187329 , version 7 (20-07-2019)

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Cite

Tatiana Labopin-Richard, Fabrice Gamboa, Aurélien Garivier, Jerome Stenger. Conditional quantile sequential estimation for stochastic codes. 2015. ⟨hal-01187329v7⟩
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