No penalty no tears: Least squares in high-dimensional linear models


Xiangyu Wang, David Dunson, Chenlei Leng ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1814-1822, 2016.


Ordinary least squares (OLS) is the default method for fitting linear models, but is not applicable for problems with dimensionality larger than the sample size. For these problems, we advocate the use of a generalized version of OLS motivated by ridge regression, and propose two novel three-step algorithms involving least squares fitting and hard thresholding. The algorithms are methodologically simple to understand intuitively, computationally easy to implement efficiently, and theoretically appealing for choosing models consistently. Numerical exercises comparing our methods with penalization-based approaches in simulations and data analyses illustrate the great potential of the proposed algorithms.

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