Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates

Mari Myllymäki, Aila Särkkä, Aki Vehtari
2014 Spatial Statistics  
Vehtari, Aki. 2014. Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates. Spatial Statistics. Volume 8. Abstract In this paper we propose a method on how to incorporate the effect of nonspatial covariates into the spatial second-order analysis of replicated point patterns. The variance stabilizing transformation of Ripley's K function is used to summarize the spatial arrangement of points, and the relationship between this summary function and
more » ... iates is modelled by hierarchical Gaussian process regression. In particular, we investigate how disease status and some other covariates affect the level and scale of clustering of epidermal nerve fibers. The data are point patterns with replicates extracted from skin blister samples taken from 47 subjects. (Mari Myllymäki), aila@chalmers.se (Aila Särkkä), aki.vehtari@aalto.fi (Aki Vehtari) Accepted to Spatial Statistics(2013), http://dx.
doi:10.1016/j.spasta.2013.07.006 fatcat:gvg7ut7nb5b7vgtb37fnwyiqpy