数学
估计员
带宽(计算)
核更平滑
变核密度估计
应用数学
数学优化
核回归
功能(生物学)
核密度估计
算法
统计
核方法
计算机科学
支持向量机
人工智能
径向基函数核
生物
进化生物学
计算机网络
作者
Jinxing Huo,Xiuqing Zhou
标识
DOI:10.1080/00949655.2023.2179626
摘要
Varying coefficient models have been studied for many years, and local kernel estimation is an important method to estimate the coefficient functions. Since same bandwidth parameter is used for each coefficient function, local kernel estimation is not effective when the degree of smoothness is different for each coefficient function. In this paper, a new global estimation method based on kernel function and backfitting is proposed. In this method, different bandwidth parameters can be used for each coefficient function to improve the estimation accuracy, and asymptotic properties of the estimators are proved. To reduce the computational complexity, an adaptive global estimation method, which is based on an estimation of the bandwidth ratio and has only one bandwidth parameter to be selected, is proposed. Simulation results show that the proposed methods work well, and two real data examples further demonstrate the potential of the proposed methods.
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