Outlier detection with robust multiple regression model I have a set of features (eg, location, income, budget, education) that I use to predict a continuous variable (say, amount spent per day on the internet). I am interested in detecting outliers. I want my model to be very strict and not to be swayed by outliers. I want my outlier detection to be done on the fly. My method is to use all the data I have so far to create a regression and then see if any point are above 3 SD from the residual mean (0). I then re-train the regression using all of the data EXCEPT the points I had just determined to be 3 SD from the residual mean. I continue this for some preset number of iterations, at each turn removing outliers and re-training. Each day I iteratively retrain the model using the new data and all of the old data. I was wondering if there is a name for this technique-- since it's the first thing I thought of, someone else must have thought of it already?

The rule that you are talking about is known and it is called 3-$\sigma$ rejection rule. This is the simplest way of robustifying the regression model. You can find anything you are searching for here.