选择最佳结点和最佳地理加权的地理加权线非参数回归模型
Sifriyani1, I Nyoman Budiantara2, Krishna Purnawan Candra3
1Study Program of Statistics, Department of Mathematics, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda 75119 Indonesia.
MethodsX
|August 6, 2024
概括
本研究引入了一种结合非参数和地理加权回归的新模型,用于分析印度尼西亚的粮食安全指数. 该模型确定了10个具有不同预测效应的区域分类,增强了空间分析.
科学领域:
- 空间统计的空间统计.
- 计量经济学 计量经济学
- 地理信息系统 (GIS) 是一个地理信息系统.
背景情况:
- 空间异质性和未知的回归函数在建模复杂数据时带来了挑战.
- 传统的参数模型可能无法捕捉空间数据模式的细微差别.
研究的目的:
- 开发一个与地理加权回归 (GWSNR) 集成的非参数回归模型,用于分析印尼省级粮食安全指数数据.
- 为了确定最佳的结点和地理权重,以提高模型的准确性.
- 根据国家粮食安全指数的重要预测指标来确定区域分组.
主要方法:
- 开发一个地理加权非参数回归 (GWSNR) 模型.
- 使用交叉验证 (CV) 和通用交叉验证 (GCV) 进行最佳结点选择.
- 采用高斯核权重和测试参数的意义,用于区域分类.
主要成果:
- 最优的GWSNR模型使用了一个高斯核,具有单个节点点,通过最低的CV和GCV值进行选择.
- 同时和部分参数测试揭示了10个不同的区域分类,具有不同的预测影响.
- 该研究成功模拟了印度尼西亚的粮食安全指数,突出了区域差异.
结论:
- 全球粮食安全指数模型有效地解决了粮食安全指数数据中的空间异质性和未知的功能形式.
- 确定的区域分组为有针对性的粮食安全干预提供了有价值的见解.
- 这种方法为国家一级指数的空间分析提供了一个强大的框架.
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