Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model

Chang-Lin Mei, Ning Wang and Wen-Xiu Zhang

Environment and Planning A, 2006, vol. 38, issue 3, 587-598

Abstract: A mixed geographically weighted regression (MGWR) model is a kind of regression model in which some coefficients of the explanatory variables are constant, but others vary spatially. It is a useful statistical modelling tool in a number of areas of spatial data analysis. After an MGWR model is identified and calibrated, which has been well studied recently, one of the important inference problems is to evaluate the influence of the explanatory variables in the constant-coefficient part on the response of the model. This is useful in the selection of the variables and for the purpose of explanation. In this paper, a statistical inference framework for this issue is suggested and, besides the F -approximation, which has been frequently used in the literature of the geographically weighted regression technique, a bootstrap procedure for deriving the p -value of the test is also suggested. The performance of the test is investigated by extensive simulations. It is demonstrated that both the F -approximation and the bootstrap procedure work satisfactorily.

Date: 2006
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:38:y:2006:i:3:p:587-598

DOI: 10.1068/a3768

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