CONDITIONAL QUANTILE SEQUENTIAL ESTIMATION FOR STOCHASTIC CODE - Archive ouverte HAL
Pré-Publication, Document De Travail Année : 2015

CONDITIONAL QUANTILE SEQUENTIAL ESTIMATION FOR STOCHASTIC CODE

Résumé

This paper is devoted to the estimation of conditional quantile, more precisely the quantile of the output of a real stochastic code whose inputs are in R d. In this purpose, we introduce a stochastic algorithm based on Robbins-Monro algorithm and on k-nearest neighbors theory. We propose conditions on the code for that algorithm to be convergent and study the non-asymptotic rate of convergence of the means square error. Finally, we give optimal parameters of the algorithm to obtain the best rate of convergence.
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Dates et 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)

Identifiants

Citer

Tatiana Labopin-Richard, F Gamboa, Aurélien Garivier. CONDITIONAL QUANTILE SEQUENTIAL ESTIMATION FOR STOCHASTIC CODE. 2015. ⟨hal-01187329v1⟩
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