Timeline for Log-concavity of the maximum of gaussians
Current License: CC BY-SA 3.0
11 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 27, 2018 at 19:32 | vote | accept | TOM | ||
| Feb 18, 2018 at 2:58 | answer | added | Iosif Pinelis | timeline score: 3 | |
| Feb 17, 2018 at 20:20 | vote | accept | TOM | ||
| Sep 27, 2018 at 19:32 | |||||
| Feb 17, 2018 at 19:09 | answer | added | ofer zeitouni | timeline score: 2 | |
| Feb 17, 2018 at 11:05 | comment | added | user64494 | @TOM: What do you mean by "distribution function":PDF or CDF? | |
| Feb 17, 2018 at 10:42 | history | edited | TOM | CC BY-SA 3.0 |
added 9 characters in body
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| Feb 17, 2018 at 10:42 | comment | added | TOM | Thank you for your observations, I indeed meant that the random variables are IID. | |
| Feb 17, 2018 at 9:34 | comment | added | user64494 | Please, clarify your question. Do you mean random variables are IID? What do you mean by "distribution function":$PDF(X,t)$ or $CDF(X,t)$? | |
| Feb 17, 2018 at 9:00 | comment | added | Brendan McKay | @PeterHeinig As your reference states, the Gumbel distribution is the limiting case as $n\to\infty$. It doesn't prove that the maximum is log-concave for finite $n$. Also, the OP didn't say that the normals are identical. | |
| Feb 17, 2018 at 8:51 | comment | added | user64494 | @Peter Heinig: Both Maple and Mathematica produce $$PDF(X,t)={\frac { \left( 1/2+1/2\,{\rm erf} \left(1/2\,t\sqrt {2}\right) \right) {{\rm e}^{-1/2\,{t}^{2}}}\sqrt {2}}{\sqrt {\pi}}} $$ in the case $n=2$ (executed codes on demand). Is this a Gumbel distribution? Can you give a reference to your statement? | |
| Feb 17, 2018 at 7:23 | history | asked | TOM | CC BY-SA 3.0 |