Nonparametric second-order estimation for spatiotemporal point patterns.
Biometrics
; 80(3)2024 Jul 01.
Article
in En
| MEDLINE
| ID: mdl-39101549
ABSTRACT
Many existing methodologies for analyzing spatiotemporal point patterns are developed based on the assumption of stationarity in both space and time for the second-order intensity or pair correlation. In practice, however, such an assumption often lacks validity or proves to be unrealistic. In this paper, we propose a novel and flexible nonparametric approach for estimating the second-order characteristics of spatiotemporal point processes, accommodating non-stationary temporal correlations. Our proposed method employs kernel smoothing and effectively accounts for spatial and temporal correlations differently. Under a spatially increasing-domain asymptotic framework, we establish consistency of the proposed estimators, which can be constructed using different first-order intensity estimators to enhance practicality. Simulation results reveal that our method, in comparison with existing approaches, significantly improves statistical efficiency. An application to a COVID-19 dataset further illustrates the flexibility and interpretability of our procedure.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Computer Simulation
/
Spatio-Temporal Analysis
/
COVID-19
Limits:
Humans
Language:
En
Journal:
Biometrics
Year:
2024
Document type:
Article