9908279ebbf1f9b250ba689db6a0222b-Reviews.html

The authors present a new method for robust principal component regression for non-Gaussian data. First, they show that principal component regression outperforms classical linear regression when the dimensionality and the sample size are allowed to increase by being insensitive to collinearity and exploiting low rank structure. They demonstrate their theoretical calculations by sweeping parameters and show that mean square error follows theory. Then the authors develop a new method for doing principal component regression by assuming the random vector and noise are elliptically distributed, a more general assumption than the standard Gaussian assumption. They demonstrate that this more general method outperforms traditional principal component regression on different elliptical distributions (multivariate-t, EC1, EC2), and show that it achieves similar performance for Gaussian distributions.