Parameter Estimation Based on Least Squares Methods

Abstract

This chapter describes how standard linear and nonlinear least squares methods can be applied to a large range of regression problems. In particular, it is shown that for many problems for which there are correlated effects it is possible to develop algorithms that use structure associated with the variance matrices to solve the problems efficiently. It is also shown how least squares methods can be adapted to cope with outliers.