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

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  • Peng Zeng
  • Yu Zhu

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.

Suggested Citation

  • Peng Zeng & Yu Zhu, 2019. "A novel regularization method for estimation and variable selection in multi-index models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(12), pages 3055-3067, June.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:12:p:3055-3067
    DOI: 10.1080/03610926.2018.1473603
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