Počet záznamů: 1

Nonlinear Functionals in Stochastic Programming; A Note on Stability and Empirical Estimatest

  1. 1.
    0348202 - UTIA-B 2011 RIV SK eng C - Konferenční příspěvek (zahraniční konf.)
    Kaňková, Vlasta
    Nonlinear Functionals in Stochastic Programming; A Note on Stability and Empirical Estimatest.
    Quantitative Methods in Economics (Multiple Criteria Decision Making XV). Bratislava, SR: University of Economics, Bratislava, 2010 - (Reiff, M.), s. 96-106. Iura Edition, člen skupiny Walters Kluwer. ISBN 978-80-8078-364-8.
    [Quantitative Methods in Economics (Multiple Criteria Decision Making). Smolenice (SK), 06.10.2010-08.10.2010]
    Grant CEP: GA ČR GAP402/10/0956; GA ČR GAP402/10/1610; GA ČR(CZ) GA402/08/0107
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: Optimization problems with a random element * One stage stochastic programming problems * Multistage stochastic programming problems * Linear and nonlinear functionals * Risk measures
    Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
    http://library.utia.cas.cz/separaty/2010/E/kankova-nonlinear functionals in stochastic programming  a note on stability and empirical estimates.pdf http://library.utia.cas.cz/separaty/2010/E/kankova-nonlinear functionals in stochastic programming a note on stability and empirical estimates.pdf

    Economic processes are very often influenced simultaneously by a decision parameter (that can be chosen according to conditions) and a random factor. Since mostly it is necessary to determine the decision parameter without knowledge of a random element realization, a deterministic optimization problem has to be defined. This deterministic problem can usually depend on an ``underlying" probability measure corresponding to the random element. The investigation of such types problems often belong to the stochastic programming field. The great attention has been focus on the problems in which objective functions depend ``linearly" on the probability measure. This note is focus on the cases when the above mentioned assumption is not fulfilled; see e.g. Markowitz functionals or some risk measures. We try to cover static (one stage problems) as well as dynamic approaches (multistage stochastic programming case
    Trvalý link: http://hdl.handle.net/11104/0188791