In Figure 8 the filter is constrained
to be of the form (1,*a _{1}*,

missif
Top is known data.
Middle includes the interpolated values.
Bottom is the filter with the leftmost point constrained
to be unity
and other points chosen to minimize output power.
Figure 8 |

The result is pleasing in that the interpolated traces
have the same general character as the given values.
The filter came out slightly different from the (1,0,-1)
that I suggested
for Figure 7
based on a subjective analysis.
Curiously, constraining the filter to be of the form (*a _{-2}*,

backwards
The filter here had its rightmost point constrained
to be unity--i.e., this filtering amounts to
backward prediction.
The interpolated data seems to be identical,
as with forward prediction.
Figure 9 |

- Objections to interpolation error
- Packing both missing data and filter into a CG vector
- Spectral preference and training data
- Summary of 1-D missing-data restoration
- 2-D interpolation before aliasing

10/21/1998