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Partially functional linear expectile regression model with missing observations

Author

Listed:
  • Chengxin Wu

    (Hefei University of Technology
    Shanghai Maritime University)

  • Nengxiang Ling

    (Hefei University of Technology)

Abstract

In this paper, we investigate estimation for the partially functional linear expectile regression model where observations are missing at random (MAR). First, we construct expectile regression (ER) estimators for both the slope functions and scalar parameters. Second, to obtain confidence intervals for the scalar parameters, we propose both the multiplier bootstrap method and the empirical likelihood (EL) method. Meanwhile, the maximum empirical likelihood (MEL) estimators for the scalar parameters are derived using the empirical log-likelihood ratio function. Furthermore, under mild conditions, we establish several asymptotic properties, including the convergence rates of the ER estimators for the scalar parameters and the slope function, the asymptotic normality of the ER estimators and the MEL estimators for the scalar parameters, and the convergence of the empirical log-likelihood ratio function to the standard chi-squared distribution. Finally, simulation studies and a real data analysis are conducted to evaluate the performance of the proposed methods.

Suggested Citation

  • Chengxin Wu & Nengxiang Ling, 2025. "Partially functional linear expectile regression model with missing observations," Computational Statistics, Springer, vol. 40(7), pages 3981-4005, September.
    • Handle: RePEc:spr:compst:v:40:y:2025:i:7:d:10.1007_s00180-025-01652-z
    DOI: 10.1007/s00180-025-01652-z
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