<p><strong>Context.</strong></p>
<p>From my experiences with this site, I've noticed standard proofs in statistics and probability theory, generally on foundational, textbook topics, tend to get answered and dealt with extremely quickly.</p>
<p>I recently posted a question about the Vapnik-Chervonenkis inequality below, and after some digging , it turns out the question is not trivial in the sense of requiring detailed comparison of two hefty proofs.</p>
<p><a href="https://stats.stackexchange.com/questions/540272/what-are-the-arguments-in-getting-from-the-theorem-of-vapnik-chervonenkis-197">What are the arguments in getting from the theorem of Vapnik &amp; Chervonenkis (1971) to the common form seen in Devroye, Gy&#246;rfi &amp; Lugosi (1996)?</a></p>
<p>Because I wasn't sure whether it appropriate to post at Cross Validated or Mathematics Stack Exchange, I placed a comment below it saying that I would migrate it myself to the latter if it wasn't appropriate. That comment received 2 upvotes, possibly indicating a degree of support for migration, but I am soliciting a more authoritative, explicit position from the community.</p>
<p><strong>Question.</strong></p>
<p><em>Are questions concerning proof in journal papers more suited for Cross Validated or for Mathematics Stackexchange? In particular, I mean proofs which are non-trivial that do not occur textbooks, but say in machine learning, theoretical statistics, and statistical learning theory journals.</em></p>
<hr />
<p><strong>Thoughts.</strong></p>
<p>On the one hand, my view is that the 'sanitised' world of abstract proof is much more in the spirit of Mathematics Stackexchange, rather than the 'messy' realism of data analysis and hence Cross Validated. This would suggest Mathematics Stackexchange for this kind of question.</p>
<p>On the other hand, it would seem that proofs in machine learning, theoretical statistics and statistical learning theory, while 'mainstream' to MSE in the sense of being proofs, are also esoteric in the use of specialised concepts, terminology, notation that are not within the realms of more traditional subfields in mathematics. In my opinion, the difficulty that this creates is that whilst I may be able to solicit assistance on parsing a proof on MSE, some of the proof steps may be motivated by specialised understanding that is more commonly found on Cross Validated.</p>
<p>As an example, how does one post a question on MSE, and reasonably expect someone there to assist in parsing a proof in the Annals of Statistics or the Journal of Machine Learning Research about theoretical guarantees on the AdaBoost algorithm if knowledge of its workings are in short supply? Clearly, proof is in the realm of Mathematics Stackexchange, but the requisite knowledge to understand its motivations is in greater supply on Cross Validated. Can someone advise?</p>
