A0544
Title: Flexible basis representations for modeling high-dimensional hierarchical spatial data
Authors: Remy MacDonald - George Mason University (United States) [presenting]
Seiyon Lee - George Mason University (United States)
Abstract: Nonstationary and non-Gaussian spatial data are prevalent across many fields (e.g., counts of animal species, disease incidences in susceptible regions, and remotely sensed satellite imagery). Due to modern data collection methods, the size of these datasets has grown considerably. Spatial generalized linear mixed models (SGLMMs) are a flexible class of models used to model nonstationary and non-Gaussian datasets. Despite their utility, SGLMMs can be computationally prohibitive for even moderately large datasets. To circumvent this issue, past studies have embedded nested radial basis functions into the SGLMM. However, two crucial specifications (knot locations and bandwidths), which directly affect model performance, are generally fixed prior to model fitting. A novel approach is proposed to model large nonstationary and non-Gaussian spatial datasets using adaptive radial basis functions. The approach: (1) partitions the spatial domain into subregions; (2) employs reversible-jump Markov chain Monte Carlo (RJMCMC) to infer the number and placement of knot locations within each partition; and (3) models the latent spatial surface using partition-varying and adaptive basis functions. Through an extensive simulation study, it is shown that the approach provides more accurate predictions than competing methods while preserving computational efficiency.
