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A General Projection Property for Distribution Families
lib:efdea24aafa25b0c (v1.0.0)
Authors: Yao-Liang Yu,Yuxi Li,Dale Schuurmans,Csaba Szepesvári
Where published: NeurIPS 2009 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/3811-a-general-projection-property-for-distribution-families
We prove that linear projections between distribution families with fixed first and second moments are surjective, regardless of dimension. We further extend this result to families that respect additional constraints, such as symmetry, unimodality and log-concavity. By combining our results with classic univariate inequalities, we provide new worst-case analyses for natural risk criteria arising in different fields. One discovery is that portfolio selection under the worst-case value-at-risk and conditional value-at-risk criteria yields identical portfolios.
Where published: NeurIPS 2009 12
Document: PDF DOI
Abstract URL: http://papers.nips.cc/paper/3811-a-general-projection-property-for-distribution-families
We prove that linear projections between distribution families with fixed first and second moments are surjective, regardless of dimension. We further extend this result to families that respect additional constraints, such as symmetry, unimodality and log-concavity. By combining our results with classic univariate inequalities, we provide new worst-case analyses for natural risk criteria arising in different fields. One discovery is that portfolio selection under the worst-case value-at-risk and conditional value-at-risk criteria yields identical portfolios.
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