Partially functional linear expectile regression model with missing observations

Chengxin Wu and Nengxiang Ling ()
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Chengxin Wu: Hefei University of Technology
Nengxiang Ling: Hefei University of Technology

Computational Statistics, 2025, vol. 40, issue 7, No 22, 4005 pages

Abstract: 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.

Keywords: Expectile regression; Empirical likelihood; Functional data analysis; Missing at random; Multiplier bootstrap (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-025-01652-z

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