# 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if a statistical technique was in current use, or if it had a heyday a couple of decades ago but it's either been superseded by newer techniques or perhaps it was even proven to be inadequate and abandoned?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?

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I googled the original paper, it has $5789$ citations, $1900+$ in 2010-2012. It seems to be quite popular. –  user10525 Jul 12 '12 at 14:29
@Procrastinator: Whoa, my google-fu is pretty bad! (Or my comprehension.) –  Wayne Jul 12 '12 at 14:37

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.

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Good tip! I just looked up the topic, apparently found the original article, and it's referenced 11 times 2010-2012, in a couple of different fields. That's reasonable, though it might also indicate people throwing an interesting technique at a problem in their field to get a publication. As you say, experience does matter. –  Wayne Jul 12 '12 at 13:18

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.

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