Previous |  Up |  Next

Article

Title: On quantile optimization problem based on information from censored data (English)
Author: Volf, Petr
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 54
Issue: 6
Year: 2018
Pages: 1156-1166
Summary lang: English
.
Category: math
.
Summary: Stochastic optimization problem is, as a rule, formulated in terms of expected cost function. However, the criterion based on averaging does not take in account possible variability of involved random variables. That is why 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 may 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 finite data sizes is studied with the aid of randomly generated example of a newsvendor problem. (English)
Keyword: optimization
Keyword: censored data
Keyword: product-limit estimator
Keyword: empirical quantile
Keyword: newsvendor problem
MSC: 62N02
MSC: 62P25
idZBL: Zbl 07031766
idMR: MR3902626
DOI: 10.14736/kyb-2018-6-1156
.
Date available: 2019-02-18T14:45:33Z
Last updated: 2020-01-05
Stable URL: http://hdl.handle.net/10338.dmlcz/147602
.
Reference: [1] Andersen, P., Borgan, O., Gill, R., Keiding, N.: Models Based on Counting Processes..Springer, New York 1993. MR 1198884, 10.1007/978-1-4612-4348-9
Reference: [2] Breslow, N., Crowley, J. E.: A large sample study of the life table and product limit estimates under random censorship..Ann. Statist. 2 (1974), 437-453. Zbl 0283.62023, MR 0458674, 10.1214/aos/1176342705
Reference: [3] Kalbfleisch, J. D., Prentice, R. L.: The Statistical Analysis of Failure Time Data (Second edition)..Wiley, New York 2002. MR 1924807, 10.1002/9781118032985
Reference: [4] Kaňková, V.: Empirical estimates in stochastic optimization via distribution tails..Kybernetika 46 (2010), 459-471. Zbl 1225.90092, MR 2676083
Reference: [5] Kibzun, A. I., Kan, Yu. S.: Stochastic Programming Problem with Probability and Quantile Functions..Wiley, Chichester 1996. 10.1016/s0166-218x(97)81420-5
Reference: [6] Kim, J. H., Powell, W.: Quantile optimization for heavy-tailed distributions using asymmetric signum functions..Working Paper, Princeton University, 2011. Retrieved 12.01.2016 from http://castlelab.princeton.edu/Papers/
Reference: [7] Peterson, A. V.: Expressing the Kaplan-Meier estimator as a function of empirical subsurvival functions..J. Amer. Stat. Assoc. 72 (1977), 360, 854-858. MR 0471165, 10.1080/01621459.1977.10479970
Reference: [8] Petruzzi, N. C., Dada, M.: Pricing and the newsvendor problem: A review with extensions..Oper. Res. 47 (1999), 2, 183-194. 10.1287/opre.47.2.183
Reference: [9] Rejto, L.: On fixed censoring model and consequences for the stochastic case..In: Trans. 9th Prague Conference on Stochastic Decision Functions 1982, Academia, Prague 1983, pp. 141-147. MR 0757919
Reference: [10] Timofeeva, G. A.: Optimal and suboptimal solutions to stochastically uncertain problem of quantile optimisation..Automat. Remote Control 68 (2007), 3, 1145-1157. MR 2341643, 10.1134/s000511790707003x
Reference: [11] Volf, P.: On precision of optimization in the case of incomplete information..Bull. Czech Econometr. Soc. 19 (2012), 170-184.
.

Files

Files Size Format View
Kybernetika_54-2018-6_5.pdf 476.4Kb application/pdf View/Open
Back to standard record
Partner of
EuDML logo