Timeline for Statistical inference under model misspecification
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
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| Jun 10, 2019 at 10:40 | comment | added | Richard Hardy | I find his writings amusing and have a lot of respect for him, but I really do have a problem understanding the point we are discussing now. Personal communication has not helped all that much as his response to my request used roughly the same arguments and similar wording to the cited texts. I think first of all, we are just having some communication problems. | |
| Jun 10, 2019 at 10:29 | comment | added | Christian Hennig | I agree that Spanos denies that there is a problem where both of us think there is one. I think something like what I proposed would be needed to investigate how big the problem actually is. Apart from his general argument, Spanos makes a few remarks why the effect of model misspecification can often be expected to be low and is non-existing in some situations. Actually you may think it is somewhat schizophrenic that Spanos argues both a) that model misspecification isn't a problem for conceptual/philosophical reasons and b) that it isn't a big problem, conceptual/philosophical reasons aside. | |
| Jun 9, 2019 at 5:23 | comment | added | Richard Hardy | To which I respond: I do not understand the argument. A commenter adds: this stuff reads mostly like expounding philosophical principles and then jumping to conclusions (for which there is no empirical or mathematical proof) by analogies and hand-waving. | |
| Jun 8, 2019 at 22:18 | comment | added | Christian Hennig | But you're right; modelling the full process with all kinds of possible modelling options would require lots of choices. I still think it'd be a worthwhile project, although not something that one could demand whenever models are selected from the same data to which they're fitted. Aris Spanos by the way argues against the idea that misspecification testing or model check on the data makes inference invalid. onlinelibrary.wiley.com/doi/abs/10.1111/joes.12200 | |
| Jun 8, 2019 at 22:14 | comment | added | Christian Hennig | There's the odd simulation in the literature exploring misspecification test/model selection first and then parametric inference conditional on the outcome of that. Results are mixed as far as I know. A "classical" example is here: tandfonline.com/doi/abs/10.1080/… | |
| Jun 7, 2019 at 17:06 | comment | added | Richard Hardy | I guess if this was feasible, it would already be in use. The main problem might be infeasibility due to the large amount of modelling choices that are data dependent (back to my first comment). Or do you not see a problem there? | |
| Jun 7, 2019 at 16:50 | comment | added | Christian Hennig | I'm not saying that this should e done every time such a situation is met in practice. It's rather a research project; however one take away message is that in my opinion, for the reasons given, data dependent model selection doesn't exactly invalidate inference that would have been valid otherwise. Such combined procedures may work rather well in many situations, although this is currently not properly investigated. | |
| Jun 7, 2019 at 15:31 | comment | added | Richard Hardy | I wonder if this is realistic in practice aside from the simplest of problems. Computational cost of simulations would quickly exceed our capabilities in most of the cases, don't you think so? Your comment on validity is of course logical. However, without this simple yet useful (in aiding our reasoning) notion we would be even more lost than we are with it - that is my perspective. | |
| Jun 7, 2019 at 13:36 | history | answered | Christian Hennig | CC BY-SA 4.0 |