论文标题
估计池二阶参数的偏置校正参数
Estimating POT Second-order Parameter for Bias Correction
论文作者
论文摘要
稳定的尾巴依赖函数提供了极端依赖结构的完整表征。不幸的是，稳定尾巴依赖函数的估计通常遭受明显的偏见，其规模与阈值峰值（POT）二阶参数有关。对于此二阶参数，本文介绍了一个受惩罚的估计器，该估计器不愿太接近零。然后，本文建立了该估计量的渐近一致性，使用它来纠正稳定尾巴依赖函数估计的偏见，并在估计极端依赖性结构时说明了其理想的经验特性。
The stable tail dependence function provides a full characterization of the extremal dependence structures. Unfortunately, the estimation of the stable tail dependence function often suffers from significant bias, whose scale relates to the Peaks-Over-Threshold (POT) second-order parameter. For this second-order parameter, this paper introduces a penalized estimator that discourages it from being too close to zero. This paper then establishes this estimator's asymptotic consistency, uses it to correct the bias in the estimation of the stable tail dependence function, and illustrates its desirable empirical properties in the estimation of the extremal dependence structures.
