A novel regularization method for estimation and variable selection in multi-index models

Peng Zeng and Yu Zhu

Communications in Statistics - Theory and Methods, 2019, vol. 48, issue 12, 3055-3067

Abstract: Multi-index models have attracted much attention recently as an approach to circumvent the curse of dimensionality when modeling high-dimensional data. This paper proposes a novel regularization method, called MAVE-glasso, for simultaneous parameter estimation and variable selection in multi-index models. The advantages of the proposed method include transformation invariance, automatic variable selection, automatic removal of noninformative observations, and row-wise shrinkage. An efficient row-wise coordinate descent algorithm is proposed to calculate the estimates. Simulation and real examples are used to demonstrate the excellent performance of MAVE-glasso.

Date: 2019
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DOI: 10.1080/03610926.2018.1473603

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