A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Identifying Latent Groups in Spatial Panel Data Using a Markov Random Field Constrained Product Partition Model
2024
Statistica sinica
Understanding the heterogeneity over spatial locations is an important problem that has been widely studied in many applications such as economics and environmental science. In this paper, we focus on regression models for spatial panel data analysis, where repeated measurements are collected over time at various spatial locations. We propose a novel class of nonparametric priors that combines Markov random field (MRF) with the product partition model (PPM) and show that the resulting prior,
doi:10.5705/ss.202021.0247
fatcat:igbmx76vo5cadawlujbx7sxji4