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

我的研究计划是开发可靠的方法来测试数据有噪声和序列相关的时间趋势或变化。它还将参与将非参数基于秩的距离与多个端点相结合，并将其应用于人口比较研究。这些主题的动机是我与环境科学、生物学和健康成果研究方面的科学家的合作活动。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途径。提出的时间序列干预分析方法将导致更有效的社会经济干预计划，特别是公共药物政策干预计划的改进设计。多终点的稳健方法将允许研究人员通过患者报告的结果来识别患者的真实行为模式。在其他主题中，我提出的研究也将致力于解决困难推理问题的强大计算机代数算法。总的来说，所提出的方法作为一般统计方法将在回答科学和社会科学中的重要问题方面为研究人员发挥重要作用。

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.