STATISTICAL INFERENCE OF GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL
STATISTICAL INFERENCE OF GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL
复制标题
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Haiyan Xuan;Shuaifeng Li;Muhammad Amin
中科院分区：
文献类型：
--
作者：
Haiyan Xuan;Shuaifeng Li;Muhammad Amin
研究了与地理和时间加权回归（GTWR）模型相关的统计推断的基本问题。首先，利用方差分析技术提出了全局平稳性、空间非平稳性和时间非平稳性假设检验问题的检验统计量。检测GTWR模型的异方差性并提供SCORE检验统计量。最后，提出了一种近似方法来计算上述检验统计量的 p 值。通过模拟研究来评估这些测试方法的性能，并给出了中国 92 个城市人均 GDP 的真实例子。
The fundamental issues of statistical inference related to geographically and temporally weighted regression (GTWR) model are studied. Initially, the test statistics for hypothesis testing problems of global stationarity, spatial nonstationarity and temporal nonstationarity are proposed by analysis of variance technique. The heteroscedasticity in GTWR model is detected and SCORE test statistic is provided. Finally, an approximation method is proposed to compute the p-values for aforementioned test statistics. A Simulat- ion study is carried out to assess the performance of these test methods, and a real example of per capita GDP in Chinese 92 cities is given.