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非参数回归估计的统一方法AI 解读

There exists a variety of nonparametric regression estimators, with cubic smoothing splines, k-nearest-neighbor (k-NN) estimators, and various types of kernel estimators among the most popular. A class of kernel estimators with local bandwidth depending on the density of the design points is introduced, where the degree of design adaptation may be expressed by a single parameter α in [0, 1]. The adaptation to the design is such that the bandwidth is made larger, to a degree…

非线性回归中的残差方差与残差模式AI 解读

A nonparametric estimator of residual variance in nonlinear regression is proposed. It is based on local linear fitting. Asymptotically the estimator has a small bias, but a larger variance compared with the parametric estimator in linear regression. Finite sample properties are investigated in a simulation study, including a comparison with other nonparametric estimators. The method is also useful for spotting heteroscedasticity and outliers in the residuals at an early…

非线性回归中的残差方差与残差模式AI 解读

A nonparametric estimator of residual variance in nonlinear regression is proposed. It is based on local linear fitting. Asymptotically the estimator has a small bias, but a larger variance compared with the parametric estimator in linear regression. Finite sample properties are investigated in a simulation study, including a comparison with other nonparametric estimators. The method is also useful for spotting heteroscedasticity and outliers in the residuals at an early…