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Weighted composite quantile regression for single index model with missing covariates at random

Author

Listed:
  • Huilan Liu

    (Guizhou University
    Guizhou University)

  • Hu Yang

    (Chongqing University)

  • Changgen Peng

    (Guizhou University)

Abstract

This paper considers weighted composite quantile estimation of the single-index model with missing covariates at random. Under some regularity conditions, we establish the large sample properties of the estimated index parameters and link function. The large sample properties of the parametric part show that the estimator with estimated selection probability have a smaller limiting variance than the one with the true selection probability. However, the large sample properties of the estimated link function indicate that whether weights were estimated or not has no effect on the asymptotic variance. Studies of simulation and the real data analysis are presented to illustrate the behavior of the proposed estimators.

Suggested Citation

  • Huilan Liu & Hu Yang & Changgen Peng, 2019. "Weighted composite quantile regression for single index model with missing covariates at random," Computational Statistics, Springer, vol. 34(4), pages 1711-1740, December.
    • Handle: RePEc:spr:compst:v:34:y:2019:i:4:d:10.1007_s00180-019-00886-y
    DOI: 10.1007/s00180-019-00886-y
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    References listed on IDEAS

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    Cited by:

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