Resources or methods for determining if a technique is in current use? I know that this question is hard to answer, but I was wondering how you would determine if a statistical technique was in current use, or if it had a heyday a couple of decades ago but it's either been superseded by newer techniques or perhaps it was even proven to be inadequate and abandoned?
For example, I was looking at a time series with R and stumbled onto the EMD package (Empirical Mode Decomposition). The technique looks like interesting, but as I google around, it seems that it never really became popular. Was it confined to the neural research community? Did wavelets prove to be superior? Did it prove to be a bit hard to interpret?
Perhaps there can be no definitive answer, but perhaps there are signs you look for?
 A: Many techniques are not so popular, however they may be useful for specific research purposes. If a statistical methodology is supported by Monte Carlo simulations, and if it was published on prestigious journals, so I think you can take it in consideration, even though it was little used by researchers. For example, I recently used the non-parametric ANCOVA developed by Conover and Iman, but to my knowledge it is not so popular in my research field. In my opinion, it is more important to have solid foundations about why you used a specific technique (robustness related to your data, Monte Carlo simulations...) in order to adequately answer possible referees concerns.
A: There's no shortcut for experience, but one way to get a hint is to look up the method's original references on google scholar and see the number of citations.
A: I agree with use4733.  My planned answer was to be "ask the experts".  Regarding this particular technique I personally can't say much because I hadn't heard of it before even though I have some expertise in time series analysis. However, since the references seem to all be over the most recent three years it could just be that it is a very new method.  New methods in almost any field take time to catch on.  So I don't think that you should assume that it is not useful.  It may eventually prove to be useful.
