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Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses

Author

Listed:
  • Xiaoshuang Zhou

    (Dezhou University)

  • Peixin Zhao

    (Chongqing Technology and Business University)

  • Yujie Gai

    (Centural University of Finance and Economics)

Abstract

In this paper, we consider the confidence interval construction for the partially nonlinear models with missing responses at random under the framework of quantile regression. We propose an imputation-based empirical likelihood method to construct statistical inferences for both the unknown parametric vector in the nonlinear function and the nonparametric function and show that the proposed empirical log-likelihood ratios are both asymptotically chi-squared in theory. Furthermore, the confidence region for the parametric vector and the pointwise confidence interval for the nonparametric function are constructed. Some simulation studies are implemented to assess the performances of the proposed estimation method, and simulation results indicate that the proposed method is workable.

Suggested Citation

  • Xiaoshuang Zhou & Peixin Zhao & Yujie Gai, 2022. "Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 705-722, December.
    • Handle: RePEc:spr:alstar:v:106:y:2022:i:4:d:10.1007_s10182-022-00441-z
    DOI: 10.1007/s10182-022-00441-z
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    References listed on IDEAS

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