平滑样条估计在非参数回归中的应用(应用于巴布亚省的贫困问题)

N. P. A. M. Mariati, I. Budiantara, V. Ratnasari
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引用次数: 2

摘要

对回归曲线的估计得到三种估计,即参数回归估计、非参数回归估计和半参数回归估计。最常用的非参数回归方法是平滑样条。平滑样条的优点是它可以在一定的子区间内使用变量数据,因此该模型需要找到自己的数据估计。平滑样条允许字符平滑地运行。在日常生活中,经常发现数据模式在一定的子间隔内发生变化,其中之一是巴布亚省的贫困数据。巴布亚省的贫困人口比例在印尼排名第一。巴布亚省贫困模型的最佳非参数平滑样条回归模型的广义交叉验证(GCV)值为92.77,R=99.99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smoothing Spline Estimator in Nonparametric Regression (Application: Poverty in Papua Province)
Three estimates were obtained in estimating the regression curve, namely estimation of parametric regression, nonparametric regression and semiparametric regression. The most popular nonparametric regression option is smoothing spline. The advantage of smoothing spline is that it can use variable data at certain sub intervals, so this model needs to find its own data estimation. Smoothing Spline allows characters to function smoothly. In everyday life, data patterns are often found to change at certain sub-intervals, one of which is poverty data in Papua Province. Papua Province is ranked first in the percentage of poor people in Indonesia. The best nonparametric Smoothing Spline regression model for the poverty model in Papua Province with a generalized cross validation (GCV) value of 92.77 and R=99.99%.
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