.png){kind=icon}
Return
Some Recent Developments in Statistics for Spatial Point Patterns
.png){kind=icon}
OA
AI
DOI：10.1146/annurev-statistics-060116-054055.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article reviews developments in statistics for spatial point processes obtained within roughly the past decade. These developments include new classes of spatial point process models such as determinantal point processes, models incorporating both regularity and aggregation, and models where points are randomly distributed around latent geometric structures. Regarding parametric inference, the main focus is on various types of estimating functions derived from so-called innovation measures. Optimality of such estimating functions is discussed, as well as computational issues. Maximum likelihood inference for determinantal point processes and Bayesian inference are also briefly considered. Concerning nonparametric inference, we consider extensions of functional summary statistics to the case of inhomogeneous point processes as well as new approaches to simulation-based inference.
Keywords:
determinantal point process
estimating function
functional summary statistic
latent geometric structure
regularity and aggregation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
8.7
Papers:
211
Citations:
2.4K
Organization
Cited Papers
ShareSaveUnsupervised deduplication using cross-field dependencies.png)
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data miningOAAI
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data miningIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more