Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/67613
Authors: 
Bazovkin, Pavel
Mosler, Karl
Year of Publication: 
2011
Series/Report no.: 
Discussion Papers in Statistics and Econometrics 6/11
Abstract: 
Linear optimization problems are investigated whose parameters are uncertain. We apply coherent distortion risk measures to capture the violation of restrictions. Such a model turns out to be appropriate for many applications and, principally, for the mean-risk portfolio selection problem. Each risk constraint induces an uncertainty set of coefficients, which comes out to be a weighted-mean trimmed region. We consider a problem with a single constraint. Given an external sample of the coefficients, the uncertainty set is a convex polytope that can be exactly calculated. If the sample is i.i.d. from a general probability distribution, the solution of the stochastic linear program (SLP) is a consistent estimator of the SLP solution with respect to the underlying probability. An efficient geometrical algorithm is proposed to solve the SLP.
Subjects: 
Robust optimization
data depth
weighted-mean trimmed regions
central regions
coherent risk measure
spectral risk measure
Document Type: 
Working Paper

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