A non-exponential extension of Sanov’s theorem via convex duality
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Publication:3298814
DOI10.1017/apr.2019.52zbMath1453.60074arXiv1609.04744OpenAlexW3023600127WikidataQ115563901 ScholiaQ115563901MaRDI QIDQ3298814
Publication date: 16 July 2020
Published in: Advances in Applied Probability (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1609.04744
stochastic optimizationweak convergencelarge deviationsheavy tailsconvex dualityrisk measuresSanov's theoremempirical measures
Large deviations (60F10) Applications of functional analysis in optimization, convex analysis, mathematical programming, economics (46N10)
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Quantitative Stability of Regularized Optimal Transport and Convergence of Sinkhorn's Algorithm ⋮ Nonexponential Sanov and Schilder theorems on Wiener space: BSDEs, Schrödinger problems and control ⋮ A characterization of transportation-information inequalities for Markov processes in terms of dimension-free concentration ⋮ Representation of weakly maxitive monetary risk measures and their rate functions ⋮ Law invariant risk measures and information divergences ⋮ Extended Laplace principle for empirical measures of a Markov chain ⋮ Conditional nonlinear expectations ⋮ Large deviations built on max-stability ⋮ Liquidity, Risk Measures, and Concentration of Measure
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