We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on γ-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tuned in a data-dependent manner. Further, the proposed method can produce robust standard error estimates and give us a reasonable quantity for local outlier detection. We demonstrate that the proposed method is superior to the existing robust geographically weighted regression through simulation and data analysis.
|Publication status||Published - 2022 Apr|
- Majorization–Minimization algorithm
- Robust divergence
ASJC Scopus subject areas
- Statistics and Probability
- Computers in Earth Sciences
- Management, Monitoring, Policy and Law