Simultaneous outlier detection and variable selection for spatial Durbin model

With the continuous development of economy and technology, the application of spatial data has become increasingly widespread. Handling complex spatial data, outlier detection has become an important problem in the study of spatial models. This article proposes a method for simultaneously performing outlier detection and variable selection in the spatial Durbin model. This method combines relevant theories of spatial statistics and enables accurate identification and location of outliers, as well as variable selection of estimation coefficients, by modeling and analyzing spatial data. The experimental results indicate that the proposed method effectively detects outliers in spatial data while maintaining accuracy, and has high interpretability and generalization value. Furthermore, a practical case is presented to demonstrate the method's effectiveness in real-world scenarios.