engleski

Similarity between random sets consisting of many components

Random sets play an essential role in modelling several phenomena in biology, medicine and material science.However, sometimes it is hard to describe them using a specific model. Therefore it can also be difficult toclassify them or to compare their realisations. This contribution proposes a similarity measure between tworandom sets whose realisations consist of many components based on just one realisation of each of them. Thesimilarity measure is obtained in a non-parametric way taking into account the shapes and the positions of thecomponents. The procedure is justified by a simulation study and consequently applied to real biomedical dataof histological images of mammary tissue.

kernel test ; non-parametric statistics ; random tessellation ; similarity measure

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