I find following commonly mentioned linear regression methods:

OLS: ordinary least squares

GLS: generalized least squares

WLS: weighted least squaes

RLM: robust linear model

OLS is usually the default. I believe robust model is to be used to correctly handle outliers, but I am not clear about others.

What are the criteria to choose one over the other?

Edit: It is mentioned in the comments that it is a very broad question (I did not know that!). However, I would like to have a one or two lines on each of above to know the "indications" or when to use them.

OLS: default

RLM: if outliers are important and cannot be ignored.

That leaves only GLS and WLS. What would be most important reasons to use them?

  • $\begingroup$ This is very general, and whole books are needed for an answer. Could you limit the scope somewhat? Linear regression methods for ... ? $\endgroup$ Jul 12, 2020 at 17:35
  • $\begingroup$ I have tried to specify that I need only a general indication on when to use each of these. $\endgroup$
    – rnso
    Jul 12, 2020 at 17:40
  • 2
    $\begingroup$ OLS is useful as pedagogically to generally introduce regression and it's concepts and assumptions, but I am not sure if or when I actually have used OLS in research. Regression methods permitting creative violation of OLS regression's assumptions permit us to model the complexities with which the world behaves with more fidelity. $\endgroup$
    – Alexis
    Jul 12, 2020 at 17:49

1 Answer 1


Your links goes to statsmodels program web pages, a software I do not know. I will assume their use of terms is the standard. A very general indication, just as a starter, what you really need is a book on regression.

  • OLS is the starting point, many other models can be seen as extensions or generalization. Assumptions is continuous response, linear effects and constant variance + independence (of residuals.)

  • GLS weakens assumption, do not assume constant variance nor independence. So you will need somehow to model the variance and covariances.

  • WLS is GLS but with covariances zero, so really an assumption of independence of residuals.

  • RLM is really a huge class of models and methods. Especially think about this for routine or automatized analyses.

  • $\begingroup$ I had asked earlier stats.stackexchange.com/questions/146077/… . But I cannot understand how to adjust options in RLM since you say that RLM is "a huge class and methods". $\endgroup$
    – rnso
    Jul 14, 2020 at 3:11
  • $\begingroup$ It is suggested at stats.stackexchange.com/questions/473603/… that WLS is useful if there is a problem of Heteroscedasticity. $\endgroup$
    – rnso
    Jul 15, 2020 at 0:55
  • $\begingroup$ Well, that is what I tried to say, do not assume constant variance. $\endgroup$ Jul 15, 2020 at 1:21
  • $\begingroup$ as far as i know, the only difference between OLS and WLS is the weights that get multiplied as part of whitening, while no whitening is applicable for OLS. given this, i would like to know, how can assumptions become less stringent in WLS from OLS and how WLS can be useful when there is heteroscedasticity? $\endgroup$ Mar 28, 2022 at 1:19

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