# Integral of CDF equals expected value

The question as below...

Let $X$ be a non-negative random variable and $F_{X}$ the corresponding CDF. Show,

$$E(X) = \int_0^\infty (1-F_X (t)) \, dt$$

in the case that, $X$ has a

a) discrete distribution b) continuous distribution

I assumed that for the case of a continuous distribution, since $F_X (t) = \mathbb{P}(X\leq t)$, then $1-F_X (t) = 1- \mathbb{P}(X\leq t) = \mathbb{P}(X> t)$. Although how useful integrating that is, I really have no idea.

Thanks for the help!

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In the two cases, it's a rewritting of the sum. Start from the RHS, that you can express in the first case as an integral of a sum and in the second as a double integral, then switch them. This is allowed because all the quantities are non-negative. – Davide Giraudo Jul 19 '12 at 13:42

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This question was asked here previously. Check and you will find a more detailed answer. Either here or on CV. – Michael Chernick Jul 19 '12 at 14:21

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– Dilip Sarwate Jul 19 '12 at 15:38

## 2 Answers

For every nonnegative random variable $X$, whether discrete or continuous or a mix of these, $$X=\int_0^{+\infty}\mathbf 1_{X\gt t}\,\mathrm dt=\int_0^{+\infty}\mathbf 1_{X\geqslant t}\,\mathrm dt,$$ hence $$\mathrm E(X)=\int_0^{+\infty}\mathrm P(X\gt t)\,\mathrm dt=\int_0^{+\infty}\mathrm P(X\geqslant t)\,\mathrm dt.$$

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Copied from Cross Validated / stats.stackexchange:

where $S(t)$ is the survival function equal to $1- F(t)$. The two areas are clearly identical.

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