Math @ Duke

Publications [#322545] of David B. Dunson
search arxiv.org.Papers Published
 Wang, X; Dunson, D; Leng, C, No penalty no tears: Least squares in highdimensional linear models,
33rd International Conference on Machine Learning, Icml 2016, vol. 4
(January, 2016),
pp. 26852706, ISBN 9781510829008
(last updated on 2019/05/25)
Abstract: © 2016 by the author(s). Ordinary least squares (OI,S) 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 threestep 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 penalizationbased approaches in simulations and data analyses illustrate the great potential of the proposed algorithms.


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