2014
DOI: 10.48550/arxiv.1405.1796
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Comparisons of penalized least squares methods by simulations

Ke Zhang,
Fan Yin,
Shifeng Xiong

Abstract: Penalized least squares methods are commonly used for simultaneous estimation and variable selection in high-dimensional linear models. In this paper we compare several prevailing methods including the lasso, nonnegative garrote, and SCAD in this area through Monte Carlo simulations. Criterion for evaluating these methods in terms of variable selection and estimation are presented. This paper focuses on the traditional n > p cases.For larger p, our results are still helpful to practitioners after the dimension… Show more

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