Quantile regression for robust inference on varying coefficient partially nonlinear models |
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Authors: | Jing Yang Fang Lu Hu Yang |
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Institution: | 1. Key Laboratory of High Performance Computing and Stochastic Information Processing (Ministry of Education of China), College of Mathematics and Computer Science, Hunan Normal University, Changsha, 410081, China;2. College of Mathematics and Statistics, Chongqing University, Chongqing, 401331, China |
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Abstract: | In this paper, we propose a robust statistical inference approach for the varying coefficient partially nonlinear models based on quantile regression. A three-stage estimation procedure is developed to estimate the parameter and coefficient functions involved in the model. Under some mild regularity conditions, the asymptotic properties of the resulted estimators are established. Some simulation studies are conducted to evaluate the finite performance as well as the robustness of our proposed quantile regression method versus the well known profile least squares estimation procedure. Moreover, the Boston housing price data is given to further illustrate the application of the new method. |
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Keywords: | 62G05 62G08 Varying coefficient partially nonlinear models Quantile regression Asymptotic properties Robustness |
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