论文标题
对空间点模式的异质性追求，并应用于树的位置：贝叶斯半参数
Heterogeneity Pursuit for Spatial Point Pattern with Application to Tree Locations: A Bayesian Semiparametric Recourse
论文作者
论文摘要
常规遇到空间点模式数据。潜在强度的灵活回归模型对于表征空间点模式并了解潜在风险因素对这种模式的影响至关重要。我们提出了一个贝叶斯半参数回归模型，其中观察到的空间点遵循具有强度函数的空间泊松过程，该过程具有强度函数，可调节具有乘法协变量效应的非参数基线强度。基线强度是分段恒定的，可以与中国餐厅的工艺相近，以防止不必要的大量作品。参数回归零件允许在回归系数上通过Spike-Slab先验进行变量选择。为提出的方法开发了有效的马尔可夫链蒙特卡洛（MCMC）算法。在广泛的模拟研究中验证了该方法的性能。在适用于巴罗科罗拉多岛森林动力学研究地图中贝尔希米木笼树的位置时，空间异质性还归因于土壤测量值的子集，除了具有空间变化的基线强度的地理测量值。
Spatial point pattern data are routinely encountered. A flexible regression model for the underlying intensity is essential to characterizing the spatial point pattern and understanding the impacts of potential risk factors on such pattern. We propose a Bayesian semiparametric regression model where the observed spatial points follow a spatial Poisson process with an intensity function which adjusts a nonparametric baseline intensity with multiplicative covariate effects. The baseline intensity is piecewise constant, approached with a powered Chinese restaurant process prior which prevents an unnecessarily large number of pieces. The parametric regression part allows for variable selection through the spike-slab prior on the regression coefficients. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for the proposed methods. The performance of the methods is validated in an extensive simulation study. In application to the locations of Beilschmiedia pendula trees in the Barro Colorado Island forest dynamics research plot in central Panama, the spatial heterogeneity is attributed to a subset of soil measurements in addition to geographic measurements with a spatially varying baseline intensity.
