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.
N. Greene. "Generalized Least-Squares Regressions II: Theory and Classification," in Proceedings of the 1st International Conference on Computational Science and Engineering (CSE '13), 2013, pp. 159-166.