Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach

Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach

Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) mode...

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Bibliographic Details
Main Authors: Rongshang Chen, Zhiyong Chen
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Entropy
Subjects:
spatial autoregressive models
partially linear varying coefficient
quantile regression
Markov chain Monte Carlo approach
Gibbs sampling
Online Access:https://www.mdpi.com/1099-4300/27/7/715
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