Spatial Autocorrelation Approaches to Testing Residuals from Least Squares Regression.

ABSTRACT: 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 statisti

SUBMITTER: Chen Y 

PROVIDER: S-EPMC4723244 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature