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Robust estimation of the SUR model
Author(s) -
Bilodeau Martin,
Duchesne Pierre
Publication year - 2000
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3315978
Subject(s) - estimator , outlier , robust regression , context (archaeology) , regression , computer science , regression analysis , robust statistics , econometrics , estimation , mathematics , statistics , artificial intelligence , machine learning , engineering , geography , archaeology , systems engineering
This paper proposes robust regression to solve the problem of outliers in seemingly unrelated regression (SUR) models. The authors present an adaptation of S ‐estimators to SUR models. S ‐estimators are robust, have a high breakdown point and are much more efficient than other robust regression estimators commonly used in practice. Furthermore, modifications to Ruppert's algorithm allow a fast evaluation of them in this context. The classical example of U.S. corporations is revisited, and it appears that the procedure gives an interesting insight into the problem.

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