A Time Domain Error Measure for Resampled Irregular Data

Resampling methods for irregularly sampled data are examined. A distinction is made between simple and complex methods. Simple methods such as Sample&Hold (S&H) and Nearest Neighbor Resampling (NNR) use only one irregular sample for one resampled observation. The advantage of simple methods is that they are robust and do not introduce a bias in the variance. A theoretical analysis as well as simulations show that NNR is more accurate than S&H. The various resampling methods are compared using the time domain error measure MET. The time domain approach has the advantage that the best possible estimates are obtained by using the data themselves. In the frequency domain approach, both allowing aliasing and applying anti-aliasing leads to distortions in the spectrum.