I have two methods which are being used to estimate a specific signal. I have a ground truth measurement of this signal and these two methods are using noisy data to estimate this signal. This signal is computed using a sliding window, meaning that two consecutive estimates are not independent (the window is 30 seconds and a new estimate is made every 1 second, so the overlap in consecutive estimates is 29 seconds). So I now have two sets of estimates - one for each method. I want to determine which method is better, i.e. which one will have smaller residuals. I am using the absolute value of these residuals for the significance test, so the test has to be non-parametric (sort of a folded normal distribution).

The two sets of estimates are paired - they are measured at the same point in time, just using different methods. I have been looking at Wilcoxon signed-rank test but there is an assumption that each pair is independent from each other, which they are not in this case.

What is the best way to perform a statistical test on paired data for which each population is not independent?

Please let me know if any clarification is required. Thanks in advance!


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.