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Robust nonnegative garrote variable selection in linear regression

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  • Gijbels, I.
  • Vrinssen, I.

Abstract

Robust selection of variables in a linear regression model is investigated. Many variable selection methods are available, but very few methods are designed to avoid sensitivity to vertical outliers as well as to leverage points. The nonnegative garrote method is a powerful variable selection method, developed originally for linear regression but recently successfully extended to more complex regression models. The method has good performances and its theoretical properties have been established. The aim is to robustify the nonnegative garrote method for linear regression as to make it robust to vertical outliers and leverage points. Several approaches are discussed, and recommendations towards a final good performing robust nonnegative garrote method are given. The proposed method is evaluated via a simulation study that also includes a comparison with existing methods. The method performs very well, and often outperforms existing methods. A real data application illustrates the use of the method in practice.

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  • Gijbels, I. & Vrinssen, I., 2015. "Robust nonnegative garrote variable selection in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 85(C), pages 1-22.
    • Handle: RePEc:eee:csdana:v:85:y:2015:i:c:p:1-22
    DOI: 10.1016/j.csda.2014.11.009
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    Cited by:

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    8. Smucler, Ezequiel & Yohai, Victor J., 2017. "Robust and sparse estimators for linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 116-130.

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