Generalized Least-Squares Regressions II: Theory and Classification

Authors

Nataniel Greene, CUNY Kingsborough Community CollegeFollow

Document Type

Presentation

Publication Date

2013

Abstract

In the first paper of this series, a variety of known and new symmetric and weighted least-squares regression methods were presented with efficient derivations. This paper continues and generalizes the previous work with a theory for deriving, analyzing, and classifying all symmetric and weighted least-squares regression methods.

Comments

This work was originally published in Proceedings of the 1st International Conference on Computational Science and Engineering (CSE '13).