Generalized Chi-squared Process for Spatial Dissimilarity

Music City Center 

Spatial dissimilarity arises from pairwise comparisons of spatially dependent groups (or ensembles). Such pairwise dissimilarities are common in various fields; for example, site-pairwise biological dissimilarities are often used to describe species diversity across a spatial domain. Statistical inference for these outcomes requires a spatial process to obtain valid results, yet considerably less attention has been devoted to constructing spatial processes indexed by pairs of locations. In this talk, we introduce a generalized chi-squared process, outline its key properties, and embed the GCP in a hierarchical spatial model that enables model-based inference for pairwise dissimilarities. The methodology is illustrated with simulation studies and ecological applications.

Bayesian hierarchical models

Biological distance

Latent factor models

Pairwise dissimilarity

Spatial generalized additive mixed models