Timeline for Convergence of the empirical distribution function
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Jan 3, 2014 at 18:13 | comment | added | adrido | for d>1, same proof goes through once you have a Glivenko-Cantelli type result. This is the case for instance if your random variables are compactly supported. I do not know what is the most general form of result you can get. I think with suitable choice of estimator (e.g. good kernel estimators instead of empirical mean) or weaker modes of convergence (e.g. in probability) you can make things work. this is perhaps a good reference: stat.washington.edu/jaw/RESEARCH/TALKS/talk2.pdf | |
| S Jan 3, 2014 at 13:51 | history | suggested | splinter123 | CC BY-SA 3.0 |
specified that the answer holds for a particular case.
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| Jan 3, 2014 at 13:39 | review | Suggested edits | |||
| S Jan 3, 2014 at 13:51 | |||||
| Jan 3, 2014 at 13:31 | comment | added | splinter123 | thanks a lot, this works in a univariate setting. But I was actually mainly concerned with the multivariate version of the result, where this kind of technique doesn't seem to be applicable... I restated the question in its more general form. | |
| S Jan 2, 2014 at 21:11 | history | suggested | András Bátkai | CC BY-SA 3.0 |
added link, formatted math
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| Jan 2, 2014 at 21:09 | review | Suggested edits | |||
| S Jan 2, 2014 at 21:11 | |||||
| Jan 2, 2014 at 20:42 | review | First posts | |||
| Jan 2, 2014 at 21:09 | |||||
| Jan 2, 2014 at 20:22 | history | answered | adrido | CC BY-SA 3.0 |