Applications of kernel regression estimation:survey
Applications of kernel regression estimation:survey
复制标题
核回归估计的应用：调查
DOI：
10.1080/03610929008830431
复制
发表时间：
1990
影响因子：
0.8
通讯作者：
P. Cheng
中科院分区：
文献类型：
--
作者：
P. Cheng
自1964年Nadaraya-Watson内核估算器以来，在文献中已经对非参数内核回归估计的主题进行了广泛的研究。在其一般形式中，问题涉及模型y = g（x）下回归函数g的估计。 + E时，观察到平滑的内核函数被观察到n。在各种收敛模式下，由于其形式的简单性和近似值，核回归估计已被证明在某些统计问题中是有用的，特别是在探索性数据分析的阶段。此外，在应用此估计过程中，我们建议使用一些丢失的数据模型的应用程序。
The topic of nonparametric kernel regression estimation has been extensively studied in the literature since the formulation of the Nadaraya-Watson kernel estimator in 1964. In its general form, the problem concerns estimation of the regression function g under the model Y = g(x) + e when a paired random sample (Xi, Yi), i=1,…,n is observed. Smoothing kernel functions have been utilized to design n weighted average estimators of the form which approximates the target function g under various modes of convergence. Due to the simplicity of its form and approximation, kernel regression estimation has been shown to be useful in some statistical problems especially at the stage of exploratory data analysis. The aim of this paper is to survey some recent research articles on the application of this estimation procedure. In addition, we recommend a possible application apropos to some missing data models.