In this paper we introduce a generalization of the Neyman-Scott process Neyman and Scott (1958) that allows for regularity in the parent process. In particular, we consider the special case where the parent process is a Strauss process with offspring points dispersed about the parent points. Such a generalization allows for point realizations that show a mix of regularity and clustering in the points. We work out a closed form approximation of the K function for this model and use this to fit the model to data. The approach is illustrated by applications to the locations of a species of trees in a rainforest dataset.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Gibbs process
- Neyman-Scott process
- Regular point process