Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model
Bibliographic Data
| ID | 7761130 |
|---|---|
| Authors | Chang-Lin Mei (Xi'an Jiaotong University), Changlin Mei (0000-0002-4663-7993, Xi'an Jiaotong University, corresponding author), Ning Wang (0009-0007-8931-918X, Xi'an Jiaotong University, corresponding author), Wen-Xiu Zhang (Xi'an Jiaotong University), Wenxiu Zhang (0009-0000-1257-5758, Xi'an Jiaotong University, corresponding author) |
| Year | 2006 |
| Volume | 38 |
| Issue | 3 |
| Pages | 587-598 |
| Publication date | 2006-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning A Economy and Space (JOURNAL) |
| Journal identifiers | ISSN: 0308-518X • E-ISSN: 1472-3409 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1068/a3768 |
| OpenAlex | W2136224105 |
| Language | EN |
| Citations received | 11 |
| References cited | 15 |
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
Cross-sectional regression · Econometrics · Inference · Linear regression · Model selection · Polynomial regression · Regression · Regression analysis · Regression diagnostic · Statistical inference · Statistics · Variables · Computer Science · Economic and Environmental Valuation · Mathematics · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Artificial Intelligence
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| Citations per year | 0.61 |
|---|---|
| Citation span | 2008 - 2025 (18) |
| Highly cited | No |