Heterogeneous Regression Models for Clusters of Spatial Dependent Data

Abstract

In economic development, there are often regions that share similar economic characteristics, and economic models on such regions tend to have similar covariate effects. In this paper, we propose a Bayesian clustered regression for spatially dependent data in order to detect clusters in the covariate effects. Our proposed method is based on the Dirichlet process which provides a probabilistic framework for simultaneous inference of the number of clusters and the clustering configurations. The usage of our method is illustrated both in simulation studies and an application to a housing cost dataset of Georgia.

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Cited by in corpus (3)

• Bayesian Spatial Homogeneity Pursuit for Survival Data with an Application to the SEER Respiratory Cancer Data
• Spatial Heterogeneity Automatic Detection and Estimation
• A Nonparametric Bayesian Item Response Modeling Approach for Clustering Items and Individuals Simultaneously