Model averaging for M-estimation |
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| Authors: | Jiang Du Zhongzhan Zhang Tianfa Xie |
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| Institution: | 1. College of Applied Sciences, Beijing University of Technology, Beijing, People's Republic of China;2. Collaborative Innovation Center on Capital Social Construction and Social Management, Beijing, People's Republic of Chinadujiang84@163.com;4. Collaborative Innovation Center on Capital Social Construction and Social Management, Beijing, People's Republic of China |
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| Abstract: | M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors or outliers in the response. Under general conditions, we develop robust versions of the focused information criterion and a frequentist model average estimator for M-estimation, and we examine their theoretical properties. In addition, we carry out extensive simulation studies as well as two real examples to assess the performance of our new procedure, and find that the proposed method produces satisfactory results. |
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| Keywords: | Model averaging estimators focused information criterion model selection model uncertainty |
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