A survey of smoothing techniques based on a backfitting algorithm in estimation of semiparametric additive models

IF 4.4 2区 数学 Q1 STATISTICS & PROBABILITY
S. E. Ahmed, D. Aydın, E. Yılmaz
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引用次数: 1

Abstract

This paper aims to present an overview of Semiparametric additive models. An estimation of the finite‐parameters of semiparametric regression models that involve additive nonparametric components is explained, including their historical background. In addition, three different smoothing techniques are considered in order to show the working procedures of the estimators and to explore their statistical properties: smoothing splines, kernel smoothing and local linear regression. These methods are compared with respect to both their theoretical and practical behaviors. A simulation study and a real data example are carried out to reveal the performances of the three methods. Accordingly, the advantages and disadvantages of each method regarding semiparametric additive models are presented based on their comparative scores using determined evaluation metrics for loss of information.
半参数加性模型估计中基于反拟合算法的平滑技术综述
本文对半参数加性模型进行了综述。解释了涉及加性非参数分量的半参数回归模型的有限参数估计,包括其历史背景。此外,为了展示估计量的工作过程并探索其统计特性,还考虑了三种不同的平滑技术:平滑样条、核平滑和局部线性回归。从理论和实践两个方面对这些方法进行了比较。通过仿真研究和实际数据实例,揭示了这三种方法的性能。因此,关于半参数加性模型的每种方法的优缺点都是基于它们的比较得分,使用确定的信息损失评估指标来呈现的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
31
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