Abstract

A fast algorithm is presented for robust estimation of a linear model with a distributed intercept. This is a regression model in which the data set contains groups with the same slopes but different intercepts, a situation which often occurs in economics. In each group, the algorithm first looks for outliers in (x,y)-space by means of a robust projection method. Then a modified version of the resampling technique is applied to the whole data set, in order to find an approximation to least median of squares or other regression methods with a positive breakdown point. Because of the preliminary projections, the number of subsets may be drastically reduced. Simulations and examples show that the overall computation time is substantially lower than that of the straightforward algorithm. The method is illustrated with a real data set.

Keywords

Algorithms, Computation time, Distributed intercept, Outlier detection, Positive-breakdown methods.