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Oct 7, 2020 at 15:40 history edited Star CC BY-SA 4.0
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Dec 12, 2018 at 15:51 comment added Star If you have time to provide an answer, I'd be glad to accept it. Thanks
Dec 12, 2018 at 15:46 comment added Star Yes, thanks, I totally understand. I modified it just because I received confusing comments and I though of clearing them up by slightly changing my question.
Dec 12, 2018 at 15:43 comment added guy @user Sorry, I’m not going to go back over your modified question :) I thought the original one was clear enough.
Dec 12, 2018 at 9:05 history edited Star CC BY-SA 4.0
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Dec 12, 2018 at 8:59 comment added Star @guy: thanks, I had to modify slightly my question given the confusion in the comments. Could you explain why $G_1=G_2$? Thanks. I can see that my condition implies $X_1-X_0 \sim X_2-X_0\sim X_0-X_1$ and $X_1-X_2 \sim X_2-X_1\sim X_2-X_0$. Why this implies $X_1-X_0\sim X_1-X_2$?
Dec 12, 2018 at 8:58 history edited Star CC BY-SA 4.0
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Dec 12, 2018 at 8:52 history edited Star CC BY-SA 4.0
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Dec 11, 2018 at 21:39 answer added Dilip Sarwate timeline score: 1
Dec 11, 2018 at 21:13 comment added Dilip Sarwate Since CDFs are right-continuous functions, your symmetry condition insists that the CDF be continuous at $0$ and have value $\frac 12$ at $0$. @Kodiologist: There exist joint CDFs for random vectors, for example, $F_{X.Y}(x,y) = P\{X \leq x, Y \leq y\}$ where the comma is commonly used to mean intersection. Easier to read than the more formal $$F_{X.Y}(x,y) = P\left(\{X \leq x\}\cap \{Y \leq y\}\right)$$ but YMMV....
Dec 11, 2018 at 19:23 comment added guy If $X_1$ is not degenerate, then a necessary condition is that the correlation between $X_1$ and $X_2$ is $0.5$. In the special case where $(X_1, X_2)$ is a bivarate normal this, with the additional requirement that the mean is $(0,0)$, is both necessary and sufficient. It is easy to find counterexamples to sufficiency in the general case (e.g., if $X_1$ and $X_2$ are Rademacher variables then this is not sufficient). Additionally, note that it is necessary that $G_1 = G_2$.
Dec 11, 2018 at 19:19 comment added Kodiologist Random vectors with more than one dimension don't have CDFs. There are marginal CDFs for the coordinates, but no one CDF for the whole joint distribution.
Dec 11, 2018 at 18:52 history asked Star CC BY-SA 4.0