title: Python statsmodel robust linear regression (RLM) outlier selection
body: <p>I'm analyzing a set of data and I need to find the regression for it. Th number of data points in the dataset are low (~15) and I decided to use the robust linear regression for the job. The problem is that the procedure is selecting some points as outlier that do not seem to be that influential. Here is scatter plot of the data, with their influence used as size:<a href="https://i.stack.imgur.com/HFIZ2.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/HFIZ2.png" alt="X vs Y. The Points B and C are selected as outliers, while point A is not."></a></p>
  
  <p>Point B and C (shown with red circle in the figure) are selected as outliers, while point A which has way higher influence is not. Although point A does not change the general trend of the regression, it is basically defining the slope along with the point with the highest X. Whereas point B and C are only affecting the significance of slope. So my question has two parts:
  1) What is the RLM package's method for selecting outliers if the most influential point is not selected and do you know of other packages that have a outlier selection that I have in mind?
  2) Do you think that point A is an outlier?</p>