Timeline for "Summing" linear regression models
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 4, 2017 at 7:15 | vote | accept | robbenhundt | ||
| Apr 4, 2017 at 7:13 | comment | added | robbenhundt | Thank you for providing the key word 'post-stratification' which really helps to dive further into the topic. | |
| Apr 3, 2017 at 21:40 | comment | added | John | @robbenhundt Multilevel models are frequently used with post-stratification to generate better higher level forecasts. | |
| Apr 3, 2017 at 21:31 | answer | added | Tim | timeline score: 2 | |
| Apr 3, 2017 at 21:03 | comment | added | Tim | First of all: it would let you build a single model for all your data instead assuming that every subsample is totally independent of every other one. | |
| Apr 3, 2017 at 20:35 | comment | added | robbenhundt | What I have just learned about multilevel models ist that they can be used for impoving the prediction at lower level by incorporating higher level information i.e. as in my case the climate setting of a city. I could not find an example where this direction is reversed which I think is the problem in this example: making predictions from low-level - trees species for a higher level - all trees. | |
| Apr 1, 2017 at 5:25 | comment | added | Tim | Why not use multilevel model? | |
| Mar 31, 2017 at 15:28 | review | First posts | |||
| Mar 31, 2017 at 15:30 | |||||
| Mar 31, 2017 at 15:27 | history | asked | robbenhundt | CC BY-SA 3.0 |