Are there any publications that were motivated by a CrossValidated post? Was the site acknowledged? Is there a badge for that?
I thought that the answers to the question "For which distributions are the parameterizations in BUGS and R different?" would be of general interest to the R community so I wrote it up (in collaboration with the answer's author). The article "Translating Probability Density Functions: From R to BUGS and Back Again." is methodological, though the research part was limited to compiling references.
The acknowledgements read "This collaboration began on the Cross Validated statistical forum (http.//stats.stackexchange.com/q/5543/1381)."
Hopefully there will be at some point. I am performing an empirical study of Nelder-Mead simplex, gradient descent and grid-search methods for model selection for kernel machines, which was inspired by a question on Cross Validated (which appropriately is also the model selection criterion ;o). No paper yet though.
Our paper summarizing this discussion: Is ridge regression useless in high dimensions ($n \ll p$)? How can OLS fail to overfit? has just been published by The Journal of Machine Learning Research:
The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization
Dmitry Kobak, Jonathan Lomond, Benoit Sanchez; 21(169):1−16, 2020.
It was a preprint since mid-2018: https://arxiv.org/abs/1805.10939 but took some time to publish formally (and got substantially extended over the course of revisions). JMLR is a very respectable journal, so I am happy :-)
We of course acknowledge CrossValidated:
@amoeba, @JonnyLomond, @BenoitSanchez
Almost two years after having asked this question, I actually wrote a paper inspired by CV (still unpublished):
I used the question's url in the body of the text.
A badge for it would be tricky, as it's not an automatic thing that can be scraped from the site itself.
I don't know of any methods research spawned by CV, but I haven't been here all that long, so someone may come and chime in. I have however used it to sanity check an idea or two that I've had. Though the one time CV did out-and-out answer the question, I had a better source provided.
I'm currently looking into the class imbalance problem, which is a very common methodological issue on this site, and have been working on a tutorial paper. The lack of an answer to my question "How do you know that your classifier is suffering from class imbalance?" suggests there are some issues with current practice that need to be addressed. Cross Validatated seems a good place for finding out what people actually do (or don't do)