What is the covariance between two binomial distributions?

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In summary, the conversation discussed a random iid sample and its empirical cdf at a given point. It was mentioned that the empirical cdf is binomially distributed with parameters n and p=F(t). The main question was to find the covariance between two empirical cdfs at different points. The result is n^{-1}F\left(s\right)\left(1-F\left(t\right)\right).
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Matteo_
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Hi everybody!

I have a random iid sample Xi, i=1, ..., n

The empirical cdf of the sample at poin s is

[tex]\hat{F}\left(s\right)=n^{-1}\sum_{i=1}^{n}{\textbf{1}_{\left(-\infty, s\right)}\left(x_{i}\right)}[/tex]

Clearly [tex]\hat{F}\left(s\right)[/tex] is binomially distributed with parameters n and p=F(t) (true cdf).

Now I need to find the covariance between [tex]\hat{F}\left(s\right)[/tex] and [tex]\hat{F}\left(t\right)[/tex] for s<t.

I know that the result is [tex]n^{-1}F\left(s\right)\left(1-F\left(t\right)\right)[/tex]

Any help is very appreciated...

Thanks!
 
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1. What is the "Cov between empirical cdf"?

The "Cov between empirical cdf" refers to the covariance between two empirical cumulative distribution functions. It measures the linear relationship between the two distributions and indicates how much they vary together.

2. How is the "Cov between empirical cdf" calculated?

The "Cov between empirical cdf" can be calculated using the formula: Cov(X,Y) = E[(X-μx)(Y-μy)], where X and Y are the two distributions, μx and μy are their respective means, and E[] represents the expected value. This formula is similar to the covariance calculation for two random variables.

3. What does a positive "Cov between empirical cdf" indicate?

A positive "Cov between empirical cdf" indicates a positive linear relationship between the two distributions. This means that as one distribution increases, the other tends to increase as well. It also suggests that the two distributions have similar variability.

4. What does a negative "Cov between empirical cdf" indicate?

A negative "Cov between empirical cdf" indicates a negative linear relationship between the two distributions. This means that as one distribution increases, the other tends to decrease. It also suggests that the two distributions have opposite variability.

5. How is the "Cov between empirical cdf" used in statistical analysis?

The "Cov between empirical cdf" is often used in statistical analysis to measure the strength and direction of the relationship between two distributions. It can also be used to compare the variability of different distributions and to identify any potential patterns or trends between them.

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