Some problems in orthogonal distance and non-orthogonal distance regression

To order the complete compilation report, use: ADA412833 The component part is provided here to allow users access to individually authored sections f proceedings, annals, symposia, etc. However, the component should be considered within-he context of the overall compilation report and not as a stand-alone technical report. Abstract Of interest here is the problem of fitting a curve or surface to given data by minimizing some norm of the distances from the points to the surface. These distances may be measured orthogonally to the surface, giving orthogonal distance regression, and for this problem, the least squares norm has attracted most attention. Here we will look at two other important criteria, the 11 norm and the Chebysbev norm. The former is of value when the data contain wild points, the latter in the context of accept/reject criteria. There are however circumstances when it is not appropriate to force the distances to be orthogonal, and two possibilities of this are also considered. The first arises when the distances are aligned with certain fixed directions, and the second when angular information is available about the measured data points. For the least squares norm, we will consider some algorithmic developments for these problems.