Time Series Analysis and Computing, and Robust Statistical Methods for Modeling Serially Correlated Data

我的研究计划是开发稳健的方法来测试时间趋势或变化，在这些趋势或变化中，数据是噪声和序列相关的。它还将致力于将基于非参数排名的距离与多个端点结合起来，并将其应用于总体比较研究。这些话题的动机是我与环境科学、生物学和健康结果研究方面的科学家们的合作活动。提出的时间序列趋势分析方法将为科学家发现水资源、野生动物种群和食物蛋白质过程的变化提供新的方法。提出的时间序列干预分析方法将导致改进设计更有效的社会经济干预方案，特别是公共药物政策干预方案。针对多个终点的强大方法将使研究人员能够通过患者报告的结果来识别患者的真实行为模式。在我提出的其他主题中，我的研究还将致力于强大的计算机代数算法，以解决困难的推理问题。总体而言，拟议的方法作为一般统计方法，将为研究人员在回答科学和社会科学中的重要问题方面发挥重要作用。

My research program is to develop robust methods in testing for temporal trends or shifts where the data are noisy and serially correlated. It will also engage in incorporating nonparametric rank-based distance with multiple endpoints and applying it to population comparison studies. The topics are motivated by my collaborative activities with scientists in environmental science, biology, and health outcomes research. The methods proposed for time series trend analysis will provide new ways for scientists to discover changes in our water resources, wildlife populations, and food protein processes. The methods proposed for time series intervention analysis will lead to improved designs for more effective socio-economic intervention programs, especially for public drug policy intervention programs. The robust methods for multiple endpoints will allow researchers to identify patients' true behaviour patterns through patient-reported outcomes. Among other topics my proposed research will also be dedicated to powerful computer algebra algorithms for resolving difficult inference questions. Overall the proposed methods as general statistical methodologies will play a significant role for researchers in answering important questions in science and the social sciences.