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reverse Markov inequality (Definition)

Let $X$ be a random variable that satisfies $\Pr(X \leq a) = 1$ for some constant $a$ Then, for $d < E[X]$ $$ \Pr( X > d) \geq \frac{E[X] - d}{a - d} $$

Proof: Apply the Markov's inequality to the random variable $\tilde{X} = a-X$ $$\Pr(X\leq d) = \Pr(\tilde{X}\geq a-d) \leq \frac{E[\tilde{X}]}{a-d} = \frac{a-E[X]}{a-d}. $$

Hence $$ \Pr(X> d) \geq 1 - \frac{a-E[X]}{a-d} = \frac{E[X]-d}{a-d}. $$




"reverse Markov inequality" is owned by kshum.
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See Also: Markov's inequality

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Cross-references: Markov's inequality, proof, random variable

This is version 3 of reverse Markov inequality, born on 2008-02-13, modified 2008-02-13.
Object id is 10264, canonical name is ReverseMarkovInequality.
Accessed 832 times total.

Classification:
AMS MSC60A99 (Probability theory and stochastic processes :: Foundations of probability theory :: Miscellaneous)

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