Should I use robust standard errors in logistic regression?

Should I use robust standard errors in logistic regression?

Should I use robust standard errors in logistic regression?

You can always get Huber-White (a.k.a robust) estimators of the standard errors even in non-linear models like the logistic regression. However, if you believe your errors do not satisfy the standard assumptions of the model, then you should not be running that model as this might lead to biased parameter estimates.

Is logistic regression robust?

The classical approach for estimating parameters is the maximum likelihood estimation, a disadvantage of this method is high sensitivity to outlying observations. Robust estimators for logistic regression are alternative techniques due to their robustness.

What does robust do 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.

Why use robust standard errors Stata?

One way to account for this problem is to use robust standard errors, which are more “robust” to the problem of heteroscedasticity and tend to provide a more accurate measure of the true standard error of a regression coefficient.

Do I need robust standard errors?

There are situations in which assumptions of the statistical model are violated leading to biased standard errors. One simple remedy is to use robust standard errors. Robust standard errors can be used when certain model assumptions involving the variance or covariance of the observations are misspecified.

What does robust regression mean in Stata?

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.

Is logistic regression robust to outliers?

However, whereas a Y value in linear regression may be arbitrarily large, the maximum fitted distance between a fitted and observed logistic value is bounded. Does that mean that a logistic regression is robust to outliers? Absolutely not.

Which method is robust to outliers?

Use a different model: Instead of linear models, we can use tree-based methods like Random Forests and Gradient Boosting techniques, which are less impacted by outliers. This answer clearly explains why tree based methods are robust to outliers.

When should you use robust regression?

What do robust standard errors tell you?

Robust standard errors, also known as Huber–White standard errors,3,4 essentially adjust the model-based standard errors using the empirical variability of the model residuals that are the difference between observed outcome and the outcome predicted by the statistical model.

Are robust standard errors efficient?

Furthermore, in case of homoscedasticity, robust standard errors are still unbiased. However, they are not efficient. That is, conventional standard errors are more precise than robust standard errors.