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

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    0348202 - ÚTIA 2011 RIV SK eng C - Conference Paper (international conference)
    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]
    R&D Projects: GA ČR GAP402/10/0956; GA ČR GAP402/10/1610; GA ČR(CZ) GA402/08/0107
    Institutional research plan: CEZ:AV0Z10750506
    Keywords : Optimization problems with a random element * One stage stochastic programming problems * Multistage stochastic programming problems * Linear and nonlinear functionals * Risk measures
    Subject RIV: BB - Applied Statistics, Operational Research
    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
    Permanent Link: http://hdl.handle.net/11104/0188791

     
     
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