Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Geograficamente Ponderada Multiescala (MGWR)× | Regressão por Mínimos Quadrados Ordinários (MQO)× | |
|---|---|---|
| Área≠ | Análise espacial | Econometria |
| Família | Regression model | Regression model |
| Ano de origem≠ | 2017 | 2019 |
| Autor original≠ | Fotheringham, Yang & Kang | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Spatially varying coefficient regression | Linear regression |
| Fonte seminal≠ | Fotheringham, A. S., Yang, W. & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Outros nomes≠ | multiscale GWR, multi-scale geographically weighted regression, Çok Ölçekli Coğrafi Ağırlıklı Regresyon (MGWR) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Relacionados | 5 | 5 |
| Resumo≠ | Multiscale Geographically Weighted Regression, introduced by Fotheringham, Yang and Kang in 2017, is a spatial regression model that lets each coefficient vary across space at its own spatial scale. It generalises Geographically Weighted Regression by giving every predictor its own bandwidth, so some relationships can act locally while others act almost globally. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
| ScholarGateConjunto de dados ↗ |
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