The development and evaluation of alternative generalized m-estimation techniques

Least squares estimation is a widely used regression technique. The presence of outliers in data can have an adverse effect on least squares estimates, resulting in a model that does not adequately fit the bulk of the data. Robust regression techniques have been proposed as an alternative to least squares when outliers are present. We develop and evaluate new robust regression procedures and compare their performance to the best alternatives currently available in terms of efficiency, breakdown, and bounded influence. We offer the better performing alternatives as possible methods for use in a robust regression scenario.