• Corpus ID: 226226784

Enveloped Huber Regression.

@article{Zhou2020EnvelopedHR,
  title={Enveloped Huber Regression.},
  author={Le Zhou and R. Dennis Cook and Hui Zou},
  journal={arXiv: Methodology},
  year={2020}
}
Huber regression (HR) is a popular robust alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from the envelope least… 

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