Regression‐type models for extremal dependence
Regression‐type models for extremal dependence
复制标题
极值依赖性的回归型模型
DOI：
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复制
发表时间：
2017
影响因子：
1
通讯作者：
V. Chavez
中科院分区：
文献类型：
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作者：
L. Mhalla;M. Carvalho;V. Chavez
我们提出了一个向量广义加性建模框架，考虑到协变量对角密度函数在多元极值背景下的影响。所提出的方法是针对极端值之间的依赖关系可能会根据协变量而变化的设置而定制的。我们设计了一个最大惩罚对数似然估计，讨论了估计过程的细节，并推导出其一致性和渐近正态性。仿真研究表明，所提出的方法在大量的仿真场景中表现良好，可以准确地恢复真实的协变量调整角密度。我们的实证分析揭示了两个高山度假胜地冬季极端气温之间的依赖关系的相关动态。
We propose a vector generalized additive modeling framework for taking into account the effect of covariates on angular density functions in a multivariate extreme value context. The proposed methods are tailored for settings where the dependence between extreme values may change according to covariates. We devise a maximum penalized log‐likelihood estimator, discuss details of the estimation procedure, and derive its consistency and asymptotic normality. The simulation study suggests that the proposed methods perform well in a wealth of simulation scenarios by accurately recovering the true covariate‐adjusted angular density. Our empirical analysis reveals relevant dynamics of the dependence between extreme air temperatures in two alpine resorts during the winter season.
DOI：
10.1080/01621459.2016.1180986
发表时间：
2016-12-01
影响因子：
3.7
作者：
Wood, Simon N.;Pya, Natalya;Saefken, Benjamin
通讯作者：
Saefken, Benjamin