Missing values in a sliding window, even at the center of the window, are simply ignored, and the number of hypotheses is reduced accordingly. Thus the smoothing methods can be applied to any window that contains at least one nonmissing value. Any -values in the input data set that fall outside the interval [0,1] are treated as missing.