Newer interface to solve nonlinear least-squares problems with bounds on the variables. FEASIBLE METHODS. differences in the algorithm, e.g. EViews offers three different methods for robust least squares: Mâestimation (Huber, 1973), S-estimation (Rousseeuw and Yohai, 1984), and MM-estimation (Yohai 1987). 4.6.3 Generalized Least Squares (GLS). An example of that which may be relevant to your problem is if try to solve least squares problem under the constraint the multiple measurements are perfectly, 100% correlated. Generalized Least Squares Generalized Least Squares. Here is a simple example using ordinary least squares: In : import numpy as np In : import statsmodels.api as sm In [3 ]: Ordinary Least Squares. squares which is an modiﬁcation of ordinary least squares which takes into account the in-equality of variance in the observations. Feasible generalized least squares (FGLS) Are the mathematics the same, just different communities (math or stats), like in Tikhonov regularization or ridge regression? GLS regression for time-series data, including diagnosis of autoregressive moving average (ARMA) models for the correlation structure of the residuals. We can use the GLSAR model with one lag, to get to a similar result: Comparing gls and glsar results, we see that there are some small It is well known that conventional SUR estimators, for example, the feasible generalized least squares (FGLS) estimator from (Zellner 1962) does not perform well in a high dimensional setting. Then βˆ GLS is the BUE for βo. Making statements based on opinion; back them up with references or personal experience.