Abstract
In this chapter, random marked closed sets are investigated. Special models with integer Hausdorff dimension are presented based on tessellations and numerical solutions of stochastic differential equations. Statistical analysis is developed which involves the random-field model test and estimation of first and secondorder characteristics. Real data analyses from neuroscience (track modeling marked by spiking intensity) and materials research (grain microstructure with disorientations of faces) are presented. Dimension reduction of point processes with Gaussian random fields as covariates was recently studied in the literature. In the present chapter this research is generalized in three different ways. Marked fibre and surface processes with covariates are subject to dimension reduction, where we restrict to the sliced inverse regression method. Slicing is suggested based on geometrical marks. In a refined model for dimension reduction the second-order central subspace is analyzed. Numerical results on estimation and testing the central subspace are presented based on simulations.
Keywords
- Point Process
- Dimension Reduction
- Random Measure
- Marked Point Process
- Slice Inverse Regression
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2015 Springer International Publishing Switzerland
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Beneš, V., Stanĕk, J., Kratochvílová, B., Šedivý, O. (2015). Random Marked Sets and Dimension Reduction. In: Schmidt, V. (eds) Stochastic Geometry, Spatial Statistics and Random Fields. Lecture Notes in Mathematics, vol 2120. Springer, Cham. https://doi.org/10.1007/978-3-319-10064-7_6
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DOI: https://doi.org/10.1007/978-3-319-10064-7_6
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10063-0
Online ISBN: 978-3-319-10064-7
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