Timeline for Choice of bin width in empirical CDF
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 21, 2020 at 9:00 | history | tweeted | twitter.com/StackStats/status/1330073523742765056 | ||
| May 22, 2018 at 13:55 | comment | added | RDK | @Tim Thank you. I meant that overfitting frequently occurs to MLE estimates as a result of relying too much on data. An oft-quoted example is estimating $P(heads)$ from 3 coin tosses which all land heads. MLE estimate would be $P(heads) = 1.0$. Would the MLE estimate of CDF be subject to a similar phenomenon in the situation that our data have noise (which they often do in the cases of interest). | |
| May 22, 2018 at 8:32 | comment | added | Tim | @RDK MLE is concerned about finding such estimate that fits your data best; it is not concerned about overfitting, on another hand, it gives you convergence promises: en.wikipedia.org/wiki/… | |
| May 22, 2018 at 5:22 | comment | added | RDK | @CliffAB Is it then reasonable to suspect that the MLE estimate would lead to overfitting? In a sense we're committing too much to the data locations. | |
| May 22, 2018 at 4:47 | comment | added | Cliff AB | It is optimal in that it is the discrete variable maximum likelihood estimate. That is, any other discrete distribution will lead to a lower likelihood. | |
| May 22, 2018 at 4:38 | history | asked | RDK | CC BY-SA 4.0 |