New nonparametric statistical methods for imperfectly observed data

针对不完全观测数据的新非参数统计方法

• 批准号: FT130100098
• 负责人: Prof Aurore Delaigle
• 金额: $ 51.39万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2014
• 资助国家: 澳大利亚
• 起止时间: 2014-06-15 至 2018-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT130100098
• 关键词: New; nonparametric; statistical; methods; imperfectly

Statistical science today is facing the challenge of having to answer questions about data that are more complex than ever before. Some of the major difficulties are caused by the lack of direct access to quantities of interest, and the more intricate structure of the available data. Motivated by applications in areas such as cancer and genetic studies, infectious disease, environmental pollution, and public health and nutrition, this project aims to develop novel and highly effective statistical methodology for solving contemporary problems involving new types of imperfectly observed data. The expected outcomes will solve frontier problems, where information can only be accessed through sophisticated computer intensive methods.

今天的统计科学正面临着必须回答比以往任何时候都更加复杂的数据问题的挑战。 一些主要困难是由于无法直接获得有关数据量，以及现有数据的结构较为复杂。受癌症和遗传学研究、传染病、环境污染、公共卫生和营养等领域的应用的启发，该项目旨在开发新颖且高效的统计方法，以解决涉及新型不完全观测数据的当代问题。预期成果将解决前沿问题，即信息只能通过复杂的计算机密集型方法获取。