Likelihood inference for complex data

复杂数据的似然推断

• 批准号: 9436-2010
• 负责人: Reid, Nancy
• 金额: $ 3.5万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=450489
• 关键词: Likelihood; inference; complex; data

Modern technology has enabled the collection of large and complex sets of data, and these are being used to answer important research questions in many fields of science and social science. Statistical methods are used to understand both the structure and the noise in this data, and new methods are being rapidly developed by statisticians working in collaboration with biologists, physicists, epidemiologists, social scientists and many others. These new methods typically involve quite complex modelling, and are computationally intensive in their implementation. In many cases these new methods are defined algorithmically in each context. The theory of statistical inference provides us with a set of guidelines for tackling new problems, provides a framework for assessing approaches developed in particular contexts, and searches for the common structure in what may seem to be very diverse problems. Statistical theory has been very successfu in finding basic structure and suggesting new solutions, and as the field develops more and more sophisticated approaches to data, there is an accompanying need for understanding the basis for the analysis.

现代技术使收集大量复杂的数据成为可能，这些数据正被用来回答许多科学和社会科学领域的重要研究问题。 统计方法被用来理解这些数据的结构和噪音，统计学家与生物学家、物理学家、流行病学家、社会科学家和许多其他人合作，正在迅速开发新的方法。 这些新方法通常涉及相当复杂的建模，并且在其实施中计算密集。 在许多情况下，这些新方法在每个上下文中都是通过算法定义的。 统计推断理论为我们提供了一套解决新问题的指导方针，为评估在特定背景下开发的方法提供了一个框架，并在看似非常不同的问题中寻找共同的结构。 统计理论在发现基本结构和提出新的解决方案方面非常成功，随着该领域开发出越来越复杂的数据方法，随之而来的是理解分析基础的需要。