Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates
Journal article, 2014

In this paper we propose a method for incorporating the effect of non-spatial 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 covariates 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 fibres. The data are point patterns with replicates extracted from skin blister samples taken from 47 subjects.

Functional data analysis

Gaussian process

K function

Epidermal nerve fibre

Replicated point pattern

Spatial point process

Author

Mari Myllymäki

Aalto University

Aila Särkkä

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

Aki Vehtari

Aalto University

Spatial Statistics

2211-6753 (ISSN)

Vol. 8 C 104-121

Subject Categories

Mathematics

Biological Sciences

Areas of Advance

Life Science Engineering

DOI

10.1016/j.spasta.2013.07.006

More information

Latest update

3/19/2018