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| Citation: |
ZENG Jie, CHENG Weihu, CHEN Haiqing. Model Averaging for Varying Coefficient Partially Linear Models With Missing Data[J]. Journal of Beijing University of Technology, 2019, 45(4): 405-412. DOI: 10.11936/bjutxb2017120029
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This paper is centered on model selection and model averaging procedure in varying coefficient partially linear models when the responses are missing at random. Under the misspecification framework, the focused information criterion (FIC) and the frequentist model average (FMA) estimator were developed based on the imputation method and the Profile least-squares technique. Then, theoretical properties of the FIC and FMA were examined. The simulation studies demonstrate the superiority of the proposed method and the approach will be applied to CD4 data.
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谭蓉. 基于复合分位数回归的变系数模型平均. 兰州文理学院学报(自然科学版). 2025(01): 29-34 .
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