We can use the same technique to throw out fitting equations from defective data that we use for missing data. Recall the theory and discussion leading up to Figure . There we identified defective data by its lack of continuity. We used the fitting equations where the weights w_{i} were chosen to be approximately the inverse to the residual (y_{i+1} -2y_{i} + y_{i-1}) itself.
Here we will first use the second derivative (Laplacian in 1-D) to throw out bad points, while we determine the PEF. Having the PEF, we use it to fill in the missing data. pefestestimate PEF in 1-D avoiding bad data The result of this ``PEF-deburst'' processing is shown in Figure .
Given the PEF that comes out of pefest1()^{}, subroutine
fixbad1() below convolves it with the data and looks for
anomalous large outputs. For each that is found, the input data is
declared defective and set to zero. Then subroutine mis1()
is invoked to replace the zeroed values by
reasonable ones.
fixbadrestore damaged data