Abstract
For a spatial point pattern observed in a bounded window, we propose using discrepancies, which are measures of uniformity in the quasi-Monte Carlo method, to test the complete spatial randomness hypothesis. Tests using these discrepancies are in fact goodness-of-fit tests for uniform distribution. The discrepancies are free from edge effects and, unlike the popular maximum absolute pointwise difference statistic of a summary function over a suitably chosen range, do not have an arbitrary parameter. Simulation studies show that they are often more powerful when a given pattern is a realization of a process with long-range interaction or a nonstationary process.
Original language | English |
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Pages (from-to) | 378-398 |
Number of pages | 21 |
Journal | Journal of Computational and Graphical Statistics |
Volume | 16 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 2007 |
Scopus Subject Areas
- Statistics and Probability
- Discrete Mathematics and Combinatorics
- Statistics, Probability and Uncertainty
User-Defined Keywords
- Complete spatial randomness
- Discrepancy
- Quasi-Monte Carlo method
- Spatial point process