Local inhomogeneous weighted summary statistics for marked point processes
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916156002009088 |
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| author | D'Angelo, Nicoletta Adelfio, Giada Mateu, Jorge Cronie, Ottmar |
| author_facet | D'Angelo, Nicoletta Adelfio, Giada Mateu, Jorge Cronie, Ottmar |
| contents | We introduce a family of local inhomogeneous mark-weighted summary statistics, of order two and higher, for general marked point processes. Depending on how the involved weight function is specified, these summary statistics capture different kinds of local dependence structures. We first derive some basic properties and show how these new statistical tools can be used to construct most existing summary statistics for (marked) point processes. We then propose a local test of random labelling. This procedure allows us to identify points, and consequently regions, where the random labelling assumption does not hold, e.g.~when the (functional) marks are spatially dependent. Through a simulation study we show that the test is able to detect local deviations from random labelling. We also provide an application to an earthquake point pattern with functional marks given by seismic waveforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_09233 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Local inhomogeneous weighted summary statistics for marked point processes D'Angelo, Nicoletta Adelfio, Giada Mateu, Jorge Cronie, Ottmar Methodology Computation We introduce a family of local inhomogeneous mark-weighted summary statistics, of order two and higher, for general marked point processes. Depending on how the involved weight function is specified, these summary statistics capture different kinds of local dependence structures. We first derive some basic properties and show how these new statistical tools can be used to construct most existing summary statistics for (marked) point processes. We then propose a local test of random labelling. This procedure allows us to identify points, and consequently regions, where the random labelling assumption does not hold, e.g.~when the (functional) marks are spatially dependent. Through a simulation study we show that the test is able to detect local deviations from random labelling. We also provide an application to an earthquake point pattern with functional marks given by seismic waveforms. |
| title | Local inhomogeneous weighted summary statistics for marked point processes |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2208.09233 |