Timeline for How to bootstrap confidence interval for large dataset
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 17, 2017 at 2:52 | vote | accept | Morrissss | ||
| Jan 16, 2017 at 2:39 | comment | added | Morrissss | @mdewey It's actually used to evaluate an AB test, so I use a t-test after estimating the variance of each experiment group. | |
| Jan 15, 2017 at 16:28 | comment | added | mdewey | Why do you want to estimate the variance? You state you actually want the confidence interval. | |
| Jan 15, 2017 at 15:46 | comment | added | Michael R. Chernick | There is a bootstrap method called m out of n bootstrap where the bootstrap sample size is m<n for n being the sample size of the original data set. Whether or not this helps you with very large n I am not sure because it requires that m/n does not go to zero with large n. so you can't conveniently take a small m and a very large n. | |
| Jan 15, 2017 at 13:59 | comment | added | user78229 | See Hastie and Efron's new book Computer Age Statistical Inference where these issues are dealt with in depth. | |
| Jan 15, 2017 at 13:58 | answer | added | SmallChess | timeline score: 2 | |
| Jan 15, 2017 at 9:39 | answer | added | Louis | timeline score: 1 | |
| Jan 15, 2017 at 9:32 | review | First posts | |||
| Jan 15, 2017 at 9:39 | |||||
| Jan 15, 2017 at 9:28 | history | asked | Morrissss | CC BY-SA 3.0 |