Gaussian Regularized Sliced Inverse Regression

  title={Gaussian Regularized Sliced Inverse Regression},
  author={Caroline Bernard-Michel and Laurent Gardes and St{\'e}phane Girard},
  journal={Statistics and Computing},
Sliced Inverse Regression (SIR) is an effective method for dimension reduction in high-dimensional regression problems. The original method, however, requires the inversion of the predictors covariance matrix. In case of collinearity between these predictors or small sample sizes compared to the dimension, the inversion is not possible and a regularization technique has to be used. Our approach is based on a Fisher Lecture given by R.D. Cook where it is shown that SIR axes can be interpreted as… CONTINUE READING
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