High dimensional data presents potential problems to standard modeling and estimation tasks commonly confronted by the data analyst. Most methods in most statistical packages do not address these issues. Robust estimation theory over the last 40 years has changed the landscape of ideas around what constitutes good practice and procedure. The goal of robust statistics is to develop data analytical methods which are resistant to outlying observations conditional on the model at hand and for a specified influence function. Such methods are able to discriminate outliers from model consistant data. LIBRA is an interesting collection of free Matlab programs designed for this very task. Further details can be found at:
www.wis.kuleuven.ac.be/stat/robust.html
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