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To me the scaled correlation trick seems equivalent to chopping up the signal into pieces and subtracting out the average for each piece, using the fact that the correlation does not record information about offset. This is odd, since the authors evaluate the efficacy of their method (for removing low frequency signals) against methods which predict a local average and subtracting that out. Instead they should say that their method is a very specific way of subtracting out a local average, which in some cases can be the best known method for analyzing the data, but there is nothing categorically novel about the method. It also seems like the method was developed after the results, and therefore this sort of analysis may have to be repeated on new data in order to eliminate the possibility of confirmation bias. (I am a physics grad student who uses signal processing techniques in my research, but I am not a peer of the authors.) Jeffeldred (talk) 13:00, 27 December 2012

Dear Jeffeldred, if you read the manuscript, you will see that the method is not developed after the results. You will see in the manuscript several equivalents of repeating the analysis on new data (real and simulated). Also, the contents of the paper may clarify some of the other concerns. (141.5.6.109 (talk) 16:32, 13 January 2015 (UTC))Reply