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2018-01-05(五),主講人:鄭明燕 教授(Hong Kong Baptist University and National Taiwan University)


統 計 學 研 究 所

專 題 演 講


講 題: Bias Reduction for Nonparametric and Semiparametric Regression Models
演講者: 鄭明燕教授(Hong Kong Baptist University and National Taiwan University)
時 間: 107年01月05日(星期五)14:00 - 15:00
地 點: 綜合三館837室
摘 要:

Nonparametric and semiparametric regression models are useful statistical regression models to discover nonlinear relationships between the response variable and predictor variables. However, optimal efficient estimators for the nonparametric components in the models are biased which hinders the development of methods for further statistical inference. In this paper, based on the local linear fitting, we propose a simple bias reduction approach for the estimation of the nonparametric regression model. Our approach does not need to use higher-order local polynomial regression to estimate the bias, and hence avoids the double bandwidth selection and design sparsity problems suffered by higher-order local polynomial fitting. It also does not inflate the variance. Hence it can be easily applied to complex statistical inference problems. We extend our approach to varying coefficient models, to estimate the variance function, and to construct simultaneous confidence band for the nonparametric regression function. Simulations are carried out for comparisons with existing methods, and a data example is used to investigate the performance of the proposed method.


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