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《Statistics & Decision》 2018-17
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Comparison Between Classical Nonparametric Regression Model and Bayesian Nonparametric Quantile Regression Model

Kong Hang;Institute of Marxism, Nanjing University of Science & Technology;  
Based on the Bayesian method,this paper deals with the quantile processing of nonparametric functions,analyzes the basic features of the function at each quantile, constructs a new nonparametric quantile regression model based on Bayesian method,and compares the estimation results with the traditional nonparametric regression model. The new model has the following advantages: Firstly, quantile difference. This model, different from the traditional non-parametric model, can analyze the differences in each quantile. Second, high efficiency. Based on the Bayesian basic method, the quantile expansion of non-parametric functions can be studied, with efficiency greatly improved. The third is its reliability. The Gibbs sample calibration results are ideal and Monte Carlo simulations have higher accuracy.
【Fund】: 国家社会科学基金青年项目(17CJL035)
【CateGory Index】: C81;F224
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