Model averaging procedure for varying-coefficient partially linear models with missing responses |
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Authors: | Jie Zeng Weihu Cheng Guozhi Hu Yaohua Rong |
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Institution: | 1. College of Applied Sciences, Beijing University of Technology, Beijing, 100124, China;2. School of Mathematics and Statistics, Hefei Normal University, Hefei, 230601, China |
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Abstract: | This paper is concerned with model averaging procedure for varying-coefficient partially linear models with missing responses. The profile least-squares estimation process and inverse probability weighted method are employed to estimate regression coefficients of the partially restricted models, in which the propensity score is estimated by the covariate balancing propensity score method. The estimators of the linear parameters are shown to be asymptotically normal. Then we develop the focused information criterion, formulate the frequentist model averaging estimators and construct the corresponding confidence intervals. Some simulation studies are conducted to examine the finite sample performance of the proposed methods. We find that the covariate balancing propensity score improves the performance of the inverse probability weighted estimator. We also demonstrate the superiority of the proposed model averaging estimators over those of existing strategies in terms of mean squared error and coverage probability. Finally, our approach is further applied to a real data example. |
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Keywords: | primary 62G08 secondary 62G20 62G99 Covariate balancing propensity score Focused information criterion Inverse probability weighted method Model averaging Varying-coefficient partially linear models |
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