Spatial Autocorrelation Approaches to Testing Residuals from Least   Squares Regression

doi:10.1371/journal.pone.0146865

CORC > 北京大学 > 地球与空间科学学院

	Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression
	Chen, Yanguang
刊名	PLOS ONE
	2016
DOI	10.1371/journal.pone.0146865
英文摘要	In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson's statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran's index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China's regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test.; SCI(E); PubMed; ARTICLE; chenyg@pku.edu.cn; 1; e0146865; 11
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/435272]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Chen, Yanguang. Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression[J]. PLOS ONE,2016.
APA	Chen, Yanguang.(2016).Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.PLOS ONE.
MLA	Chen, Yanguang."Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression".PLOS ONE (2016).