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.

我的提议包括两个项目，第一个项目涉及统计方法，第二个项目与气候中的统计应用有关。 我的第一个项目着重于经验可能性方法，该方法使我们能够从数据中推断人口特征。它不需要精心设计的型号，并且可以充分利用当今可用的便宜且快速的计算能力。例如，人们可以使用此方法计算人口真实平均高度的间隔估计，而不知道人口的身高分布。经验可能的方法是80年代后期斯坦福大学Art Owen教授提出的一种现代推理方法。现在，它已经发展成为一个重要而强大的统计学家工具。我已经为经验可能性理论做出了贡献。我现在的重点是处理技术约束的问题，该技术约束会影响经验可能性方法的准确性。这是一个重要的问题，它吸引了包括UBC的Jihua Chen教授和Owen教授本人在内的著名研究人员的注意。我希望找到更好的解决方案。 我的第二个项目与历史温度重建方法有关，这对于理解全球变暖至关重要。从工业革命时期（大约1850年）开始，我们只有温度记录。要欣赏近几十年来的变暖，我们需要一个长期的历史观点，该观点需要在1850年之前对全球温度进行可靠的估计/重建。我的项目将研究一个统计模型，用于使用代理数据（例如树环和冰核数据和其他气候变量）估算历史温度的统计模型。这是与全球变暖的研究直接相关的重要问题。统计学家和气候学家以及政府都感兴趣。