Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates
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© 2014 Elsevier BV. This is the post print version of the following article: Myllymäki, Mari & Särkkä, Aila & Vehtari, Aki. 2014. Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates. Spatial Statistics. Volume 8. 104-121. ISSN 2211-6753 (printed). DOI: 10.1016/j.spasta.2013.07.006, which has been published in final form at http://www.sciencedirect.com/science/article/pii/S1053811912000845.
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School of Science |
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2014
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Language
en
Pages
104-121
Series
Spatial Statistics, Volume 8
Abstract
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.Description
Keywords
Epidermal nerve fibre, Functional data analysis, Gaussian process, K function, Replicated point pattern, Spatial point process
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Citation
Myllymäki, Mari & Särkkä, Aila & Vehtari, Aki. 2014. Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates. Spatial Statistics. Volume 8. 104-121. ISSN 2211-6753 (printed). DOI: 10.1016/j.spasta.2013.07.006.