论文标题
使用空间标记点过程的汇总计数和尺寸的联合建模，具有亚物体标记分布
Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions
论文作者
论文摘要
为了准确量化土耳其地区的滑坡危险，我们在贝叶斯分层框架内开发了新的标记点过程模型，以共同预测滑坡计数和尺寸。为了适应少数最大的滑坡总体大小的主要作用，我们利用极值理论的强烈理由利用标记分布，从而弥合了极端和标记点模式的两个广泛统计区域。在数据级别上，我们假设对滑坡计数的泊松分布，而我们比较了滑坡尺寸的不同“亚物质”分布，以灵活地模拟其上下尾巴。在潜在水平上，泊松强度和大小分布的中位数在固定和随机效果方面在空间上有所不同，共享空间组件捕获了压倒性计数和尺寸之间的互相关。我们使用固有条件自回归先验的固有的空间依赖性对空间依赖进行了牢固的模型。使用定制的自适应马尔可夫链蒙特卡洛算法有效地拟合了我们的新型模型。我们表明，对于我们的数据集，与更传统的选择相比，亚物质标记分布提供了改进的大型滑坡大小的预测。为了展示联合发生尺寸模型的好处，并说明了它们对风险评估的有用性，我们沿着主要道路绘制了滑坡危险。
To accurately quantify landslide hazard in a region of Turkey, we develop new marked point process models within a Bayesian hierarchical framework for the joint prediction of landslide counts and sizes. To accommodate for the dominant role of the few largest landslides in aggregated sizes, we leverage mark distributions with strong justification from extreme-value theory, thus bridging the two broad areas of statistics of extremes and marked point patterns. At the data level, we assume a Poisson distribution for landslide counts, while we compare different "sub-asymptotic" distributions for landslide sizes to flexibly model their upper and lower tails. At the latent level, Poisson intensities and the median of the size distribution vary spatially in terms of fixed and random effects, with shared spatial components capturing cross-correlation between landslide counts and sizes. We robustly model spatial dependence using intrinsic conditional autoregressive priors. Our novel models are fitted efficiently using a customized adaptive Markov chain Monte Carlo algorithm. We show that, for our dataset, sub-asymptotic mark distributions provide improved predictions of large landslide sizes compared to more traditional choices. To showcase the benefits of joint occurrence-size models and illustrate their usefulness for risk assessment, we map landslide hazard along major roads.
