Timeline for Am I doing hierarchical bayesian regression?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Apr 17, 2019 at 19:59 | comment | added | Patrick | simple answer: because I don't know how to do hierarchical modelling! I'm just starting to read about it (and I'm not sure I really understand the concept of hyperpriors). Stay tuned for future questions! :) Thanks again Tim. | |
| Apr 17, 2019 at 19:55 | vote | accept | Patrick | ||
| Apr 17, 2019 at 19:40 | comment | added | Tim | @Patrick if it was separate data, then it would be typical case of Bayesian updating. I get the argument with small subgroup, it makes sense. But standard Bayesian way would be to use hierarchical model that estimates both, higher and lower level effects, that can deal with such cases. Why not use it? | |
| Apr 17, 2019 at 18:26 | comment | added | Patrick | Understood @Tim. And if it were new data, it would be ok, right? And to answer your questions, I thought that it would be a good idea, after having estimated parameters for each group A,B,C to reinject this information into the submodels so if submodel (A,a1) for example does not have a lot of data, at least it is "guided" by top level parameters of model A. | |
| Apr 17, 2019 at 18:06 | comment | added | Tim | @Patrick first, you would be using same data twice, so the model would be overconfident. It's like you found some information on encyklopedia Britannica and cross-referenced it with Wikipedia that quoted as a source Britannica... Second, you would gain nothing by this as you are using the same data, so why not simply train a different model? What would be the benefit of using such "priors"? | |
| Apr 17, 2019 at 16:08 | comment | added | Patrick | Thanks @Tim, very appreciated. What is not clear for me is the following: suppose I conducted an analysis last year but only on the highest order grouping (groups A,B,C). So three models with 2 parameters each ($\beta_{0}$ and $\beta_{1}$) have been built. Now suppose I want to extend these models to include the subgroups. Can't I use the information gained from the first analysis? I thought that bayesian learning was exactly that: incorporate new insights to refine models, isn't that true? | |
| Apr 16, 2019 at 20:11 | history | answered | Tim | CC BY-SA 4.0 |