King's College London

Research portal

NONPARAMETRIC BOOTSTRAP FOR K-FUNCTIONS ARISING FROM MIXED-EFFECTS MODELS WITH APPLICATIONS IN NEUROPATHOLOGY

Research output: Contribution to journalArticle

Sabine Landau, Ian P. Everall

Original languageEnglish
Pages (from-to)1375 - 1393
Number of pages19
JournalSTATISTICA SINICA
Volume18
Issue number4
Publication statusPublished - Oct 2008

King's Authors

Abstract

Neuropathological studies frequently determine the positions of cells on multiple brain tissue sections taken from multiple individuals. Interest arises in group comparisons of the spatial dependencies between cells, in particular the spatial dependencies of a single cell type (clustering or regularity as measured by the univariate K-function), or the spatial interaction of two different cell types (attraction or repulsion as measured by the bivariate K-function). While the nonparametric statistical analysis of spatial dependencies in the one-way design is fairly well-established, investigations often employ more complex designs. In this paper we develop a residual bootstrapping approach for K-functions arising from a general repeated measures design by assuming an underlying linear mixed-effects model. We illustrate our methodology by re-analysing the spatial interaction between neurons and astrocytes (brain cells that are functionally related to neurons) in a study of HIV associated dementia.

View graph of relations

© 2018 King's College London | Strand | London WC2R 2LS | England | United Kingdom | Tel +44 (0)20 7836 5454