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Robust and efficient direction identification for groupwise additive multiple-index models and its applications

Author

Listed:
  • Kangning Wang

    (Shandong Technology and Business University
    Chongqing University of Arts and Sciences)

  • Lu Lin

    (Shandong University)

Abstract

This paper concerns robust and efficient direction identification for a groupwise additive multiple-index model, in which each additive function has a single-index structure. Interestingly, without involving non-parametric approach, we show that the directions of all the index parameter vectors can be recovered by a simple linear composite quantile regression (CQR). As a specific application, a iterative-free CQR estimation procedure for the partially linear single-index model is proposed. Furthermore, it can also be used to develop a penalized CQR procedure for variable selection in the high-dimensional settings. The new method has superiority in robustness and efficiency by inheriting the advantage of the CQR approach. Simulation results and real-data analysis also confirm our method.

Suggested Citation

  • Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    • Handle: RePEc:spr:testjl:v:26:y:2017:i:1:d:10.1007_s11749-016-0496-0
    DOI: 10.1007/s11749-016-0496-0
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    References listed on IDEAS

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