(7) |

(8) |

The detailed form of these weighting functions is not important here.
The form I chose is somewhat arbitrary and may be far from optimal.
The choice of the constant is discussed on page .
What is more important is the idea that
instead of minimizing the sum of *errors themselves,*
we are minimizing something like the sum of **relative error**s.
Weighting makes any region of the data plane
as important as any other region,
regardless of whether a letter (big signal) is present
or absent (small signal).
It is like saying a zero-valued signal is just as important
as a signal with any other value.
A zero-valued signal carries information.

When signal strength varies over a large range, a nonuniform weighting function should give better regressions. The task of weighting-function design may require some experimentation and judgment. |

- A nonlinear-estimation method
- Clarity of nonlinear picture
- Nonuniqueness and instability
- Estimating the noise variance
- Colored noise

10/21/1998