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Biomedical subjects

J Chadoeuf

Publications and source records attributed to J Chadoeuf.

2 recordsLinked to original sources
PubMed2001

Testing local independence between two point processes.

Dependencies between two types of points in a spatial point process can be due either to a real dependence between the two types or to the dependence on common underlying variables. We propose a global test for dependence between two point processes that is valid for a wide range of models. In contrast with previously proposed methods, it is based on a number of local test statistics, which makes it possible to map the local association between the two processes. The behavior of the test is evaluated by a simulation study. It is then applied to a vegetation pattern data set from Burkina Faso.

Burkina Faso↗
PubMed2000

Testing local dependence of spatial structures on images

Associations between two spatial processes can be due to a real dependence between the two processes or to the dependence on common underlying variables. We propose to test the existence of a real dependence by use of local tests, leading to a global test of real dependence and a map of local interactions. We present first how classical interaction tests based on random rotations between completely observed processes such as those developed by Berman (Berman. Appl. Statist. (1986) 35, 54-62), can be integrated in local analyses. For this purpose, tests are first performed locally, and the distribution of their p-values is then compared to the corresponding value under the null hypothesis. A similar approach is proposed to test non-stationarity of a point pattern by using distance statistics popularized by Diggle (Diggle. Statistical Analysis of Spatial Point Patterns. (1983) Academic Press, New York). The problem of testing the interaction between a random field and a censoring area pattern process is discussed and an approach similar to the preceding ones is then proposed. The methods are mainly applied to agricultural examples but they can be applied to any microscopical images for which one wishes to analyse the spatial structure.

Journal Article↗