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- W3126702966 abstract "When covariate information is collected through opinion surveys, experimental science, medical research and so on, incomplete records may result in missing information. Expectile regression is used in many fields; however, many fewer developments have been made with expectile regression for data with missing information. In this paper, we develop a weighted expectile regression approach for estimating the conditional expectile when covariates are missing at random (MAR). The asymptotic normality of the proposed weighted estimators is established. A proximal alternating direction method of multipliers (pADMM) algorithm is developed for the calculation of the weighted estimator. Simulation studies are conducted to assess the finite-sample performance of the proposed method. We illustrate the application of the method by analyzing a real data set from Beijing Olympic sports center monitoring stations." @default.
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- W3126702966 date "2021-01-29" @default.
- W3126702966 modified "2023-10-16" @default.
- W3126702966 title "Weighted expectile regression with covariates missing at random" @default.
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- W3126702966 doi "https://doi.org/10.1080/03610918.2021.1873371" @default.
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