| Ementa: |
1. Regressão Espacial: O que são dados espaciais, Matriz de proximidades espacial, Estatísticas espaciais globais e locais; 2. Modelos de regressão espacial global: SAR, SEM, SAC, Durbin, MESS; 3. Modelos de regressão espacial local: Golden Section Search, Normal, Poisson, Binomial Negativo, Beta. |
| Referências: |
Anselin, L. (1988), Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, USA.Fotheringham, A. S. Brunsdon, C. Charlton, M. (2000), Quantitative Geography: Perspectives on Spatial Data Analysis, SAGE, London.Fotheringham, A. S. Brunsdon, C. Charlton, M. (2002), Geographically Weighted Regression: the analysis of spatially varying relationships, Wiley, England.LeSage, J. P. and Pace, K. (2007), A Matrix Exponential Spatial Specification. Journal of Econometrics, 140 (1), pp.190–214.Nakaya, T., Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2005). Geographically Weighted Poisson Regression for Disease Association Mapping. Statistics in Medicine, 24, pp. 2695 – 2717.Silva, A. R. Mendes, F. F. (2018), On comparing some algorithms for finding the optimal bandwidth in geographically weighted regression. Applied Soft Computing v. 73, p. 943-957.Silva, A. R. Lima, A. O. (2017), Geographically Weighted Beta Regression. Spatial Statistics v. 21, p. 279-303.Silva, A. R. e Rodrigues, T. C. V. (2014). Geographically weighted negative binomial regression - incorporating overdispersion. Statistics and Computing, 24 (5), pp. 769-783. |
