A1006
Title: Bayesian spatial envelope model
Authors: Renhao Sun - University of Texas at San Antonio (United States) [presenting]
Reisa Widjaja - University of Wisconsin - La Crosse (United States)
Wenbo Wu - University of Texas at San Antonio (United States)
Abstract: The recently developed Bayesian framework for the envelope model enhances the interpretability of model parameters and simplifies the incorporation of prior information. However, the current method presumes an independent error structure within the model and fails to account for the additional complexities introduced by spatially correlated data. Therefore, a Bayesian framework is proposed for the spatial envelope model under a linear coregionalization model framework, which allows a heterogeneous spatial covariance structure for different variables in the response vector. By incorporating the prior information of model parameters, the proposed method offers a more flexible and robust framework for capturing uncertainty and spatial correlation in high-dimensional data. Furthermore, appropriate prior specifications are investigated for the spatial parameters, the propriety of the posterior distribution is studied, and a posterior sampling method is developed to sample both the posterior distributions of spatial parameters and the conditional posterior distributions associated with the envelope model.
