When would you use a robust regression?

Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations.

What does it mean for a regression to be robust?

Robust regression is an alternative to least squares regression when data are contaminated with outliers or influential observations, and it can also be used for the purpose of detecting influential observations.

Is OLS same as linear regression?

2 Answers. Yes, although ‘linear regression’ refers to any approach to model the relationship between one or more variables, OLS is the method used to find the simple linear regression of a set of data. Linear regression refers to any approach to model a LINEAR relationship between one or more variables.

What is the purpose of the robust option in Stata?

robust helps implement estimation commands and is rarely used. That is because other commands are implemented in terms of it and are easier and more convenient to use. For instance, if all you want to do is make your estimation command allow the vce(robust) and vce(cluster clustvar) options, see [R] ml.

What does VCE robust do?

vce(robust) uses the robust or sandwich estimator of variance. This estimator is robust to some types of misspecification so long as the observations are independent; see [U] 20.21 Obtaining robust variance estimates.

What is robust standard error?

“Robust” standard errors is a technique to obtain unbiased standard errors of OLS coefficients under heteroscedasticity. “Robust” standard errors have many labels that essentially refer all the same thing. Namely, standard errors that are computed with the sandwich estimator of variance.

What is Huber regression?

Huber regression (Huber 1964) is a regression technique that is robust to outliers. The idea is to use a different loss function rather than the traditional least-squares; we solve. minimizeβ∑mi=1ϕ(yi−xTiβ) for variable β∈Rn, where the loss ϕ is the Huber function with threshold M>0, ϕ(u)={u2if |u|≤M2Mu−M2if |u|>M.