Regularized Least Absolute Deviations Regression and an Efficient Algorithm for Parameter Tuning

@article{Wang2006RegularizedLA,
  title={Regularized Least Absolute Deviations Regression and an Efficient Algorithm for Parameter Tuning},
  author={Li Wang and Michael D. Gordon and Ji Zhu},
  journal={Sixth International Conference on Data Mining (ICDM'06)},
  year={2006},
  pages={690-700}
}
Linear regression is one of the most important and widely used techniques for data analysis. However, sometimes people are not satisfied with it because of the following two limitations: 1) its results are sensitive to outliers, so when the error terms are not normally distributed, especially when they have heavy-tailed distributions, linear regression often works badly; 2) its estimated coefficients tend to have high variance, although their bias is low. To reduce the influence of outliers… CONTINUE READING
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