Wavelet-Based Bayesian Hierarchical Modeling with Regularized Horseshoe Priors for Spatially Correlated Functional Data
空间相关函数数据的小波贝叶斯层级模型与正则化马蹄先验
Environmental monitoring networks increasingly record pollutant levels as curves observed over time at fixed stations. Yet, exposure and risk assessment often require predicting these curves, with their uncertainty, at unmonitored locations. Conventional smooth-basis methods for such spatially correlated functional data, including spline-based kriging, tend to over-smooth acute, high-frequency episodes such as pollution peaks. We propose a parsimonious wavelet-based Bayesian…