Partially adaptive robust estimation of regression models and applications

This paper provides an accessible exposition of recently developed partially adaptive estimation methods and their application. These methods are robust to thick-tailed or asymmetric error distributions and should be of interest to researchers and practitioners in data mining, agent learning, and mathematical modeling in a wide range of disciplines. In particular, partially adaptive estimation methods can serve as robust alternatives to ordinary regression analysis, as well as machine learning methods developed by the artificial intelligence and computing communities.Results from analysis of three problem domains demonstrate application of the theory.