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Statistical Inference of Semidefinite Programming
Alexander Shapiro (ashapiro Abstract: In this paper we consider covariance structural models with which we associate semidefinite programming problems. We discuss statistical properties of estimates of the respective optimal value and optimal solutions when the `true' covariance matrix is estimated by its sample counterpart. The analysis is based on perturbation theory of semidefinite programming. As an example we consider asymptotics of the so-called Minimum Trace Factor Analysis. We also discuss the Minimum Rank Matrix Completion problem and its SDP counterparts. Keywords: Semidefinite Programming, Minimum Trace Factor Analysis, Matrix Completion problem, minimum rank, nondegeneracy, statistical inference, asymptotics Category 1: Linear, Cone and Semidefinite Programming (Semi-definite Programming ) Category 2: Stochastic Programming Citation: Download: [PDF] Entry Submitted: 01/31/2017 Modify/Update this entry | ||
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