1. رسولی، ح. ر. (۱۳۹۰)، مدلهای اتورگرسیو فضایی و تحلیل دادههای معاملات مسکونی شهر تهران، مجله علوم آماری، ۵، ۱۸۹-۲۰۲. 2. محمدزاده، م. (۱۳۹۸)، آمار فضایی و کاربردهای آن، چاپ سوم، مرکز نشر آثار علمی دانشگاه تربیت مدرس، تهران. 3. Akaike, H. (1976). An information criterion (AIC). Mathematical Sciences, 14, 5-7. 4. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Vol. 4, Springer Series, London. [ DOI:10.1007/978-94-015-7799-1] 5. Anselin, L. (1990). Spatial Dependence and Spatial Structural Instability in Applied Regression Analysis. Journal of Regional Science, 30(2), 185-207. [ DOI:10.1111/j.1467-9787.1990.tb00092.x] 6. Bille, A. G., Benedetti, R., and Postiglione, P. (2017). A Two-Step Approach to Account for Unobserved Spatial Heterogeneity. Spatial Economic Analysis, 4, 81-91. [ DOI:10.1080/17421772.2017.1286373] 7. Bowman, A. W. (1984). An Alternative Method of Cross-Validation for the Smoothing of Density Estimates. Biometrika, 71(2), 353-360. [ DOI:10.1093/biomet/71.2.353] 8. Breusch, T. S., and Pagan, A. R. (1979). A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica: Journal of the Econometric Society, 47(5), 1287-1294. [ DOI:10.2307/1911963] 9. Cleveland, W. S. (1979). Robust Locally Weighted Regression on Smoothing Scatterplots. Journal of the American Statistical Association, 74(368), 829-836. [ DOI:10.1080/01621459.1979.10481038] 10. Cressie, N. A. (1993). Statistics for Spatial Data. Wiley Series in Probability and Mathematical Statistics, New York.
https://doi.org/10.1002/9781119115151.ch1
https://doi.org/10.1002/9781119115151 [ DOI:10.1002/9781119115151.ch5] 11. Ferguson, T. S. (1973). A Bayesian Analysis of Some Nonparametric Problems. The Annals of Statistics, 1(2), 209-230. [ DOI:10.1214/aos/1176342360] 12. Fotheringham, A. S., Brundson, C., and Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley, Chichester, UK. 13. Fotheringham, A. S., Yang, W., and Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. [ DOI:10.1080/24694452.2017.1352480] 14. Geniaux, G., and Martinetti, D. (2018). A New Method for Dealing Simultaneously With Spatial Autocorrelation and Spatial Heterogeneity in Regression Models. Regional Science and Urban Economics, 72, 74-85. [ DOI:10.1016/j.regsciurbeco.2017.04.001] 15. Gomez-Rubio, V., Bivand, R. S., and Rue, H. (2021). Estimating Spatial Econometrics Models With Integrated Nested Laplace Approximation. Mathematics, 9(17), 2044. [ DOI:10.3390/math9172044] 16. Hurvich, C. M., Simonoff, J. S., and Tsai, C. L. (1998). Smoothing Parameter Selection in Nonparametric Regression Using an Improved Akaike Information Criterion. Journal of the Royal Statistical Society, Series B, 60(2), 271-293. [ DOI:10.1111/1467-9868.00125] 17. Kelejian, H. H., and Robinson, D. P. (1998). A Suggested Test for Spatial Autocorrelation and/or Heteroskedasticity and Corresponding Monte Carlo Results. Regional Science and Urban Economics, 28(4), 389-417. [ DOI:10.1016/S0166-0462(98)00007-6] 18. Kelejian, H. H., and Prucha, I. R. (2010). Specification and Estimation of Spatial Autoregressive Models With Autoregressive and Heteroskedastic Disturbances. Journal of Econometrics, 157(1), 53-67. [ DOI:10.1016/j.jeconom.2009.10.025] [ PMID] [ ] 19. Lawson, A. B., Choi, J., and Zhang, J. (2014). Prior Choice in Discrete Latent Modeling of Spatially Referenced Cancer Survival. Statistical Methods in Medical Research, 23(2), 183-200. [ DOI:10.1177/0962280212447148] [ PMID] 20. LeSage, J. P., and Pace, R. K. (2009). Introduction to Spatial Econometrics. Chapman and Hall/CRC, London. [ DOI:10.1201/9781420064254] 21. LeSage, J. P., Pace, K. R., Lam, N., Campanella, R., and Liu, X. (2011). New Orleans Business Recovery in the Aftermath of Hurricane Katrina. Journal of the Royal Statistical Society, Series A, 174, 1007-1027. [ DOI:10.1111/j.1467-985X.2011.00712.x] 22. Li, F., and Sang, H. (2019). Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets. Journal of the American Statistical Association, 114(527), 1050-1062. [ DOI:10.1080/01621459.2018.1529595] 23. Li, Z., Fotheringham, A. S., Oshan, T. M., and Wolf, L. J. (2020). Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights. Annals of the American Association of Geographers, 110(5), 1500-1520. [ DOI:10.1080/24694452.2019.1704680] 24. Mohammadzadeh, M. (2019). Spatial Statistics and Its Applications. Tarbiat Modares University Press Center, 3th Edition, Tehran. 25. Moran, P. A. (1948). Some Theorems on Time Series: The Significance of the Serial Correlation Coefficient. Biometrika, 35, 255-260.
https://doi.org/10.1093/biomet/35.3-4.255 [ DOI:10.2307/2332344] 26. Mur, J., López, F., and Angulo, A. (2008). Symptoms of Instability in Models of Spatial Dependence. Geographical Analysis, 40(2), 189-211. [ DOI:10.1111/j.1538-4632.2008.00719.x] 27. Polzehl, J., and Spokoiny, V. (2000). Adaptive Weights Smoothing With Applications to Image Restoration. Journal of the Royal Statistical Society, Series B, 62, 335-354. [ DOI:10.1111/1467-9868.00235] 28. Rasouli, H R. (2012). Spatial Autoregressive Models and Data Analysis of Residential Transactions in Tehran. Journal of Statistical Sciences, 5, 189-202. 29. Rue, H., Martino, S., and Chopin, N. (2009). Approximate Bayesian Inference for Latent Gaussian Models Using Integrated Nested Laplace Approximations. Journal of the Royal Statistical Society, Series B, 71, 319-392. [ DOI:10.1111/j.1467-9868.2008.00700.x] 30. Silverman, B. W. (1984). A Fast and Efficient Cross-Validation Method for Smoothing Parameter Choice in Spline Regression. Journal of the American Statistical Association, 79, 584-589. [ DOI:10.1080/01621459.1984.10478084] 31. Stein, A. (2022). The Development of the Journal Spatial Statistics: The First 10 Years. Spatial Statistics, 50, 100576. [ DOI:10.1016/j.spasta.2021.100576] 32. Sugasawa, S. (2021). Grouped Heterogeneous Mixture Modeling for Clustered Data. Journal of the American Statistical Association, 116(534), 999-1010. [ DOI:10.1080/01621459.2020.1777136] 33. Zhao, P., Yang, H. C., Dey, D. K., and Hu, G. (2023). Spatial Clustering Regression of Count Value Data via Bayesian Mixture of Finite Mixtures. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 3504-3512. [ DOI:10.1145/3580305.3599509] 34. Zhihua, M., Yishu, X., and Guanyu, H. (2020). Heterogeneous Regression Models for Clusters of Spatial Dependent Data. Spatial Economic Analysis, 15(4), 459-475. [ DOI:10.1080/17421772.2020.1784989]
|