Bayesian inference for nonstationary marginal extremes
Bayesian inference for nonstationary marginal extremes
复制标题
非平稳边际极值的贝叶斯推理
DOI：
--
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
P. Jonathan
中科院分区：
文献类型：
--
作者：
D. Randell;K. Turnbull;K. Ewans;P. Jonathan
我们提出了一个简单的分段模型的样本的峰值超过阈值，非平稳的多维协变量，并估计它使用一个精心设计的和计算效率的贝叶斯推理。模型参数本身使用惩罚B-样条表示作为协变量的函数进行参数化。这允许对非平稳极端环境进行详细表征。该方法给出了类似的推论，一个可比的频率论惩罚最大似然法，但计算效率更高，并允许在一个单一的建模步骤中的不确定性更完整的表征。我们使用该模型来量化的联合方向和季节变化的风暴峰值显着波高在北海北方的位置和估计预测的方向，季节性的回报值分布所需的设计和可靠性评估的海洋和沿海结构。
We propose a simple piecewise model for a sample of peaks‐over‐threshold, nonstationary with respect to multidimensional covariates, and estimate it using a carefully designed and computationally efficient Bayesian inference. Model parameters are themselves parameterized as functions of covariates using penalized B‐spline representations. This allows detailed characterization of non‐stationarity extreme environments. The approach gives similar inferences to a comparable frequentist penalized maximum likelihood method, but is computationally considerably more efficient and allows a more complete characterization of uncertainty in a single modelling step. We use the model to quantify the joint directional and seasonal variation of storm peak significant wave height at a northern North Sea location and estimate predictive directional–seasonal return value distributions necessary for the design and reliability assessment of marine and coastal structures.
Langevin 扩散和 Metropolis 调整 Langevin 算法
影响因子：
0.8
作者：
Xifara T
通讯作者：
Xifara T