Transformation and weighting in regression
Transformation and weighting in regression
Numerical methods of statistics
Numerical methods of statistics
Modern Applied Statistics with S
Modern Applied Statistics with S
Editorial: Nonparametric and Robust Methods
Computational Statistics & Data Analysis
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Bounded-influence estimation is a well developed and useful theory. It provides fairly efficient estimators which are robust to outliers and local model departures. However, its use has been limited thus far, mainly because of computational difficulties. A careful implementation in modern statistical software can effectively overcome the numerical problems of bounded-influence estimators. The proposed approach is based on general methods for solving estimating equations, together with suitable methods developed in the statistical literature, such as the delta algorithm and nested iterations. The focus is on Mallows estimation in generalized linear models and on optimal bias-robust estimation in models for independent data, such as regression models with asymmetrically distributed errors.