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dc.contributor.authorRockafellar, R.T.
dc.contributor.authorRoyset, J.O.
dc.contributor.authorS.I. Miranda
dc.date2013-08-03
dc.date.accessioned2014-01-09T22:24:09Z
dc.date.available2014-01-09T22:24:09Z
dc.date.issued2013-08-03
dc.identifier.citationR.T. Rockafellar, J.O. Royset, and S.I. Miranda,"Superquantile Regression with Applications to Buffered Reliability, Uncertainty Quantification, and Conditional Value-at-Risk," European Journal of Operational Research, to appear.
dc.identifier.citationRockafellar, R. Terry, Johannes O. Royset, and Sofia I. Miranda. "Superquantile regression with applications to buffered reliability, uncertainty quantification, and conditional value-at-risk." European Journal of Operational Research 234.1 (2014): 140-154.
dc.identifier.urihttps://hdl.handle.net/10945/38224
dc.description.abstractThe paper presents a generalized regression technique centered on a superquantile (also called conditional value-at-risk) that is consistent with that coherent measure of risk and yields more conservatively fitted curves than classical least-squares and quantile regression. In contrast to other generalized regression techniques that approximate conditional superquantiles by various combinations of conditional quantiles, we directly and in perfect analog to classical regression obtain superquantile regression functions as optimal solutions of certain error minimization problems. We show the existence and possible uniqueness of regression functions, discuss the stability of regression functions under perturbations and approximation of the underlying data, and propose an extension of the coefficient of determination R-squared for assessing the goodness of fit. The paper presents two numerical methods for solving the error minimization problems and illustrates the methodology in several numerical examples in the areas of uncertainty quantification, reliability engineering, and financial risk management.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleSuperquantile Regression with Applications to Buffered Reliability, Uncertainty Quantification, and Conditional Value-at-Risken_US
dc.typeArticleen_US
dc.contributor.departmentOperations Research (OR)


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