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

Sabine Landau and Ian P. Everall
Statistica Sinica
Vol. 18, No. 4 (October 2008), pp. 1375-1393
Stable URL: http://www.jstor.org/stable/24308560
Page Count: 19
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
NONPARAMETRIC BOOTSTRAP FOR K-FUNCTIONS ARISING FROM MIXED-EFFECTS MODELS WITH APPLICATIONS IN NEUROPATHOLOGY
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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.

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