Probability Inequalities
暂无分享,去创建一个
For a set A, let m A = inf{g(t); t ∈ A} for a positive function g. Then The Chebyshev inequality occurs by taking g to be function increasing on the support of X and A = [x, ∞), then m A = g(x), Eg(X) ≥ g(x)P {X > x} or P {X > x} ≤ Eg(X) g(x). This can be seen graphically in Figure 1 for the case g(x) = x. The area of the rectangle xP {X > x} is less than EX, the area above the graph of the cumulative distribution function and below the line y = 1.
[1] Norbert Kusolitsch,et al. Maß- und Wahrscheinlichkeitstheorie : Eine Einführung , 2014 .
[2] E. Lehmann. Some Concepts of Dependence , 1966 .
[3] I. Gijbels,et al. Positive quadrant dependence tests for copulas , 2010 .
[4] V. V. Petrov. Limit Theorems of Probability Theory: Sequences of Independent Random Variables , 1995 .