Timeline for Fitting t-distribution in R: scaling parameter
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| S Nov 26, 2018 at 20:36 | history | suggested | vkehayas | CC BY-SA 4.0 |
minor typos fixed
|
| Nov 26, 2018 at 20:09 | review | Suggested edits | |||
| S Nov 26, 2018 at 20:36 | |||||
| May 22, 2016 at 12:04 | history | edited | Scortchi♦ | CC BY-SA 3.0 |
added references
|
| May 22, 2016 at 11:40 | comment | added | Scortchi♦ | (+1) "Unbounded above" isn't a wrong answer & might well be useful for some purposes when coupled with an interval estimate. The important thing is not to blindly use the observed Fisher information to form Wald confidence intervals. | |
| Feb 12, 2016 at 20:00 | comment | added | user12719 | Your conclusion that the problems of estimating df might actually work against the reason to choose a t-distribution in the first place (i.e. robustness) is thought provoking. | |
| Feb 11, 2016 at 19:06 | history | answered | kjetil b halvorsen♦ | CC BY-SA 3.0 |