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人们的广泛关注。该建议开发了新的程序，以克服在不同的设置，如：估计方程，平稳序列，高维数据，Pickands的依赖函数在多元极值分布和Copula在风险管理中的推理问题中应用这些方法的计算负担。使用经验似然方法的一个实际障碍是由于高维数据、讨厌参数的大尺寸以及数据的依赖性而引起的计算问题。研究人员希望开发新的方法来克服计算问题并扩展潜在的应用范围，新的研究成果可以应用于经济学、保险学、金融学、风险管理和其他需要有效工具来探索多元序列之间的非线性相关性并增加模型复杂性的社会科学。

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.