Robust Regression
نویسنده
چکیده
An introduction to robustness in statistics, with emphasis on its relevance to regression analysis. The weaknesses of the least squares estimator are highlighted, and the idea of error in data re ned. Properties such as breakdown, e ciency and equivariance are discussed and, through consideration of M, S and MM-estimators in relation to these properties, the progressive nature of robust estimator development is demonstrated. Finally, some (often overlooked) limitations of favoured `high breakdown, high e ciency' estimators are considered, and based on results obtained from simulations, guidance is given and cautions proposed regarding the application of such techniques. This piece of work is a result of my own work except where it forms an assessment based on group project work. In the case of a group project, the work has been prepared in collaboration with other members of the group. Material from the work of others not involved in the project has been acknowledged and quotations and paraphrases suitably indicated.
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