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Semi-parametric rank regression with missing responses

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  • Bindele, Huybrechts F.
  • Abebe, Ash

Abstract

We consider a semi-parametric regression model with responses missing at random and study the rank estimator of the regression coefficient. Consistency and asymptotic normality of the proposed estimator are established. Monte Carlo simulation experiments show that the proposed estimator is more efficient than the least squares estimator whenever the error distribution is heavy tailed or contaminated. When the errors follow a normal distribution, these simulation experiments show that the rank estimator can be more efficient than its least squares counterpart for cases with large proportion of missing responses.

Suggested Citation

  • Bindele, Huybrechts F. & Abebe, Ash, 2015. "Semi-parametric rank regression with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 117-132.
    • Handle: RePEc:eee:jmvana:v:142:y:2015:i:c:p:117-132
    DOI: 10.1016/j.jmva.2015.08.007
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    References listed on IDEAS

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    Cited by:

    1. Ash Abebe & Huybrechts F. Bindele & Masego Otlaadisa & Boikanyo Makubate, 2021. "Robust estimation of single index models with responses missing at random," Statistical Papers, Springer, vol. 62(5), pages 2195-2225, October.

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