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Journal Article

On quantile optimization problem based on information from censored data

Volf Petr

: Kybernetika vol.54, 6 (2018), p. 1156-1166

: GA18-02739S, GA ČR

: optimization, censored data, empirical quantile

: 10.14736/kyb-2018-6-1156

: http://library.utia.cas.cz/separaty/2019/SI/volf-0498944.pdf

(eng): Stochastic optimization problem is, as a rule, formulated in terms of expected cost function. However, the criterion considered in the present contribution uses selected quantiles. Moreover, it is assumed that the stochastic characteristics of optimized system are estimated from the data, in a non-parametric setting, and that the data\nmay be randomly right-censored. Therefore, certain theoretical results concerning estimators of distribution function and quantiles under censoring are recalled and then utilized to prove consistency of solution based on estimates. Behavior of solutions for fi nite data sizes is studied with the aid of randomly generated example of a newsvendor problem.

: BB

: 10103