Stochastic variational inequalities (SVI) model a large class of equilibrium problems subject to data uncertainty, and are closely related to stochastic optimization problems. The SVI solution is usually estimated by a solution to a sample average approximation (SAA) problem. This paper considers the normal map formulation of an SVI, and proposes a method to build asymptotically exact condence regions and condence intervals for the solution of the normal map formulation, based on the asymptotic distribution of SAA solutions. The condence regions are single ellipsoids with high probability. We also discuss the computation of simultaneous and individual condence intervals.
Department of Statistics and Operations Research, University of North Carolina at Chapel Hill
View Symmetric confidence regions and confidence intervals for normal map formulations of stochastic variational inequalities