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I have a empirical cumulative probability distribution function for a random variable. The random variable is "time to failure" and I have the full curve i.e till the probability reaches 1. I want to know Mean Time To Failure i.e expectation of that random variable. Is there any standard method to find mean from an empirical distribution.

I am getting the empirical CDF (as discrete values) as output from a "model checking tool" which uses iterative numerical computation techniques to get those probabilities. For example, let F(x)=P(X<=t) is the CDF of the random variable X where X stands for time between failure. To plot the curve of F(X) vs t I am varying t with some step size, calculating F(X) for that t using the "model checking tool" and adding the points to get the curve. I can use small step size to get the more accurate curve. So, I have access to only this CDF values at different t. From this values I want to do a good estimate of mean value of X.

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    $\begingroup$ It is considered somewhat impolite to simultaneously post (or nearly so, in your case) the same question in multiple forums. See the various meta sites for more details. Crosspost: math.stackexchange.com/questions/61460/… $\endgroup$ – cardinal Sep 3 '11 at 4:18
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    $\begingroup$ well, it's a fact that $E(X) = \int_{0}^{\infty} \big(1-F(x) \big) dx$ for a non-negative random variable $X$ with cdf $F$. Perhaps you can use this fact while invoking the convergence of the empirical cdf to the true one to get an estimate. $\endgroup$ – Macro Sep 3 '11 at 4:25
  • $\begingroup$ @cardinal: Sorry for posting it twice. Actually, someone in math exchange has suggested me to post in stats.stackexchange as it is relevant to statistics $\endgroup$ – RIchard Williams Sep 3 '11 at 4:40
  • $\begingroup$ You've received pretty explicit answers there, including the one that @Macro lists. Where do your remaining doubts lie? $\endgroup$ – cardinal Sep 3 '11 at 4:49
  • $\begingroup$ Let's resolve it this way -- please edit the Q to express your remaining doubts or it will be closed. $\endgroup$ – user88 Sep 3 '11 at 11:26
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I know this is an older question, but others may find the answer helpful.

Given the empirical CDF, $F_n(x)$, call the percent points of the CDF $\alpha$ (which range from $0$ to $1$) and their corresponding values VaR$_\alpha$ (Value at Risk). VaR$_\alpha$ is simply $F_n^{-1}(\alpha)$ You can use the fact that: $$ E(X) = \int_0^1 VaR_\alpha\;d\alpha $$

This is actually the dual of the relationship @Macro stated, however, instead of adding up vertical slices across the x-axis from $F(x)$ to $1$, you are adding horizontal slices from the y-axis to F(x) up to $y=1$. It is the same area.

To actually do the integration, I'd recommend the trapezoid rule, so given $n$ entries in the CDF we have: $$ E(X) \approx \sum_{k=0}^{n-1} \frac{VaR_{k+1} + VaR_{k}}{2}\cdot\left(\alpha_{k+1}-\alpha_{k}\right) $$

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    $\begingroup$ Given you're on a stats site, you should probably use a more conventionally-statistical term in place of VaR. At the very least, you should define it -- I don't imagine the average psych- or bio- or medical- stats person has encountered VaR. Outside of places like banking/finance and insurance, it's pretty rare to encounter it -- and even within banking vs insurance, there can be differences in how it's looked at. $\endgroup$ – Glen_b -Reinstate Monica Sep 5 '13 at 2:32
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    $\begingroup$ @Glen_b that is an excellent point, thank you, and I have edited the answer to provide a more well-known definition. As someone who deals in insurance math, I appreciate that I may be guilty of thinking it its terms, which can be different from more generalized statistical terms. $\endgroup$ – Avraham Sep 8 '13 at 13:39
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    $\begingroup$ Beyond @Glen_b's point about VaR, percentiles are values of $X$, not the associated cumulative probabilities. For example, $\alpha = 0.5$ defines the median, but the median is the percentile, not $\alpha$. Some people say percent points, which may be where you are getting confused. $\endgroup$ – Nick Cox Sep 8 '13 at 23:43
  • $\begingroup$ Much obliged for the clarification of terminology. $\endgroup$ – Avraham Sep 9 '13 at 3:11

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