A generalized Neyman-Pearson Lemma for \(g\)-probabilities

DOI10.1007/s00440-009-0244-4zbMath1197.93163OpenAlexW2080704658WikidataQ124987250 ScholiaQ124987250MaRDI QIDQ707608

D. Kharzeev

Publication date: 8 October 2010

Published in: Probability Theory and Related Fields (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00440-009-0244-4

zbMATH Keywords

hypothesis teststochastic maximum principlebackward stochastic differential equationNeyman-Pearson lemma\(g\)-probability/expectation

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Optimal stochastic control (93E20) Applications of stochastic analysis (to PDEs, etc.) (60H30) Optimality conditions for problems involving randomness (49K45)

Related Items (11)

Stochastic maximum principle for mean-field forward-backward stochastic control system with terminal state constraints ⋮ Backward Stackelberg Differential Game with Constraints: A Mixed Terminal-Perturbation and Linear-Quadratic Approach ⋮ An optimal insurance design problem under Knightian uncertainty ⋮ A maximum principle for controlled time-symmetric forward-backward doubly stochastic differential equation with initial-terminal state constraints ⋮ The optimal control problem with state constraints for fully coupled forward-backward stochastic systems with jumps ⋮ The optimal portfolio selection model under \(g\)-expectation ⋮ An optimal control problem of forward-backward stochastic Volterra integral equations with state constraints ⋮ Maximum principle for a stochastic delayed system involving terminal state constraints ⋮ Recursive utility optimization with concave coefficients ⋮ A maximum principle for fully coupled forward-backward stochastic control systems with terminal state constraints ⋮ Recursive utility maximization for terminal wealth under partial information

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