Collaborative Research: Reducing Computation in Empirical Likelihood Methods

经验似然方法的研究在统计学的许多领域引起了广泛的关注。本提案开发了新的程序，以克服将这些方法应用于不同情况下的推理问题的计算负担，例如：估计方程，平稳序列，高维数据，多元极值分布中的Pickands依赖函数和风险管理中的copulds。使用经验似然方法的一个实际障碍是由于高维数据、大尺寸的讨厌参数以及数据中的依赖性而导致的计算问题。研究人员打算开发新的方法来克服计算问题并扩大潜在应用的范围。新的研究成果可以应用于经济、保险、金融、风险管理等社会科学领域，这些领域需要有效的工具来探索多元序列之间的非线性依赖关系，增加模型的复杂性。

The study of empirical likelihood methods has attracted much attention in many areas of statistics. This proposal develops new procedures to overcome the computational burden in applying these methods to inference problems in different settings such as: estimating equations, stationary sequences, high-dimensional data, Pickands dependence functions in multivariate extreme-value distributions and copulas in risk management. A practical obstacle in using empirical likelihood methods is the computational issue due to the high dimensional data, the large size of nuisance parameters as well as dependence in the data. The investigators intend to develop new methodologies to overcome the computational problems and extend the scope of the potential applications.The new research results can be applied to economics, insurance, finance, risk management and other social sciences that require effective tools for exploring nonlinear dependence among multivariate series and to increase the complexity of the models.