Name
Abstract | ||
|---|---|---|
Abstract Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s10109-016-0239-5 | Journal of Geographical Systems |
Keywords | Field | DocType |
Geographically weighted regression, GWR, Collinearity, Regression diagnostics, C18 Methodological issues: general, C52 Model evaluation, validation, and selection | Econometrics,Collinearity,Geographically Weighted Regression,Regression,Regression diagnostic,Multicollinearity,Statistics,Geography | Journal |
Volume | Issue | ISSN |
18 | 4 | 1435-5949 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Stewart Fotheringham | 1 | 143 | 33.77 |
| Taylor Oshan | 2 | 0 | 0.34 |