Abstract
The most commonly used regression methods are the LAD estimation, invented by Boscovich (1757) for estimating the shape of the earth; the LS regression developed by Legendre (1805) for determining the orbits of comets; the M-regression introduced by Huber (1973) as a method insensitive to small deviation from an idealized model; and the TLS regression suggested by Koenker and Bassett (1978), comparably efficient as the LS for the Gaussian linear models and outperforming the LS estimation over a wide class of non-Gaussian error distributions. The ultimate goal of our book was to develop some adaptive methods of estimation in linear regression models based on convex combinations of pairs of the above procedures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer Science+Business Media New York
About this chapter
Cite this chapter
Dodge, Y., Jureĉková, J. (2000). Epilogue. In: Adaptive Regression. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8766-2_11
Download citation
DOI: https://doi.org/10.1007/978-1-4419-8766-2_11
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4612-6464-4
Online ISBN: 978-1-4419-8766-2
eBook Packages: Springer Book Archive