Cox processes driven by transformed Gaussian processes on linear networks—A review and new contributions
本文针对线性网络上点过程模型缺乏的问题，提出了三类Cox过程模型（对数高斯、中断和永久Cox过程），并首次给出参数族的统计方法和应用，同时构建了新的模拟算法。
Abstract There is a lack of point process models on linear networks. For an arbitrary linear network, we consider new models for a Cox process with an isotropic pair correlation function obtained in various ways by transforming an isotropic Gaussian process which is used for driving the random intensity function of the Cox process. In particular, we introduce three model classes given by log Gaussian, interrupted, and permanental Cox processes on linear networks, and consider for the first time statistical procedures and applications for parametric families of such models. Moreover, we construct new simulation algorithms for Gaussian processes on linear networks and discuss whether the geodesic metric or the resistance metric should be used for the kind of Cox processes studied in this paper.