2019/11/16 by Morteza Raeisi, Raeisi, Morteza, Florent Bonneu +3 · 18 citations
Economics, Econometrics and Finance · Mathematics · #60G55 #62H30 #62M30 #62P12 #Applications (stat.AP) #Artificial intelligence #Cartography #Computer science #Data mining #Econometrics #FOS: Computer and information sciences #Geography #Inference #Mathematics #Methodology (stat.ME) #Point (geometry) #Point process #Point processes and geometric inequalities #Process (computing) #Scale (ratio) #Spatial and Panel Data Analysis #Statistics #msc:60G55 #msc:62H30 #msc:62M30 #msc:62P12 #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.1911.06999
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2019/11/16 · arxiv created 2020/12/17 · arxiv updated 2020/12/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Because most natural phenomena exhibit dependence at multiple scales like locations of earthquakes or forest fire occurrences, spatio-temporal single-scale point process models are unrealistic in many applications. This motivates us to construct generalizations of classical Gibbs models. In this paper, we extend the Geyer saturation point process model to the spatio-temporal multi-scale framework. The simulation process is carried out through a birth-death Metropolis-Hastings algorithm. In a simulation study, we compare two common methods for statistical inference in Gibbs models: the pseudo-likelihood and logistic likelihood approaches that we tailor to this model. Finally, we illustrate this new model on forest fire occurrences modeling in Southern France.