Empirical likelihood methods and statistical applications in climate studies

气候研究中的经验似然方法和统计应用

• 批准号: 194404-2011
• 负责人: Tsao, Min
• 金额: $ 1.17万
• 依托单位: University of Victoria
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569779
• 关键词: Empirical; likelihood; methods; statistical; applications

My proposal consists of two projects, the first of which is concerned with statistical methodology and the second is concerned with statistical applications in climatology. My first project focuses on the empirical likelihood method which enables us to make inference on population characteristics from data. It does not require elaborate models and takes full advantage of the inexpensive and fast computing power available today. For example, one may use this method to compute an interval estimate for the true average height of a population without knowing the height distribution of the population. The empirical likelihood method is a modern method of inference introduced by Professor Art Owen at Stanford University in the late 80's. It has now evolved into an important and powerful tool for statisticians. I have already made contributions to the empirical likelihood theory. My focus now is on the problem of handling a technical constraint which affects the accuracy of the empirical likelihood method. This is an important problem which has attracted the attention of prominent researchers including Professor Jiahua Chen at UBC and Professor Owen himself. I hope to find a better solution to the problem. My second project is concerned with methods for historical temperature reconstruction which is important for understanding global warming. We only have temperature records from the time of the industrial revolution (roughly 1850) and onwards. To appreciate the warming in recent decades, we need a long-term historical perspective which requires reliable estimation/reconstruction of global temperature before 1850. My project will study a statistical model for estimating the historical temperature using proxy data such as tree ring and ice core data and other climate variables. This is an important problem directly related to research on global warming. It is of interest to statisticians and climatologists as well as the government.

我的提案由两个项目组成，第一个项目涉及统计方法，第二个项目涉及气候学中的统计应用。 我的第一个项目侧重于经验似然法，它使我们能够从数据中推断人口特征。它不需要复杂的模型，并充分利用当今可用的廉价且快速的计算能力。例如，人们可以使用这一方法来计算人群的真实平均身高的区间估计，而不需要知道人群的身高分布。经验似然法是斯坦福大学Art Owen教授在20世纪80年代末提出的一种现代推理方法。它现已发展成为统计学家的重要而强大的工具。我已经对经验似然理论做出了贡献。我现在的重点是处理影响经验似然法准确性的技术约束问题。这是一个重要的问题，引起了包括 UBC 陈嘉华教授和 Owen 教授本人在内的著名研究人员的关注。我希望找到更好的方法来解决这个问题。 我的第二个项目涉及历史温度重建方法，这对于理解全球变暖非常重要。我们只有工业革命时期（大约 1850 年）及以后的温度记录。为了了解近几十年来的变暖情况，我们需要一个长期的历史视角，这需要对 1850 年之前的全球温度进行可靠的估计/重建。我的项目将研究一种统计模型，用于使用树木年轮和冰芯等代理数据来估计历史温度数据和其他气候变量。这是与全球变暖研究直接相关的重要问题。统计学家、气候学家以及政府都对此感兴趣。