Improved Semi-Parametric Bounds for Tail Probability and Expected Loss: Theory and Applications
尾概率与期望损失的改进半参数界:理论与应用
Many management decisions involve accumulated random realizations for which only the first and second moments of their distribution are available. The sharp Chebyshev-type bound for the tail probability and Scarf bound for the expected loss are widely used in this setting. We revisit the tail behavior of such quantities with a focus on independence. Conventional primal-dual approaches from optimization are ineffective in this setting. Instead, we use probabilistic…