Statistical challenges involving indirect data

涉及间接数据的统计挑战

• 批准号: DP170102434
• 负责人: Prof Aurore Delaigle
• 金额: $ 23.72万
• 依托单位: The University of Melbourne
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2018
• 资助国家: 澳大利亚
• 起止时间: 2018-01-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP170102434
• 关键词: Statistical; challenges; involving; indirect; data

This project aims to develop statistical methodology for solving contemporary problems involving indirectly observed data whose complexity is exacerbated by factors such as incompleteness or episodic availability. Modern statistics find it difficult to analyse complex data which contain important information only in an indirect way, such as data measured with noise or aggregated data. This project considers both finite dimensional data and functional data. The expected methodology will be able to solve frontier problems, where only sophisticated methods can access information. This is expected to benefit brain studies, economics, infectious disease, nutrition and public health.

该项目旨在开发统计方法，以解决涉及间接观察数据的当代问题，这些数据的复杂性因不完整或时断时续的可获得性等因素而加剧。现代统计学发现，很难分析只以间接方式包含重要信息的复杂数据，如用噪声测量的数据或聚合数据。该项目同时考虑了有限维数据和函数数据。预期的方法将能够解决前沿问题，在那里只有复杂的方法才能获取信息。预计这将使大脑研究、经济学、传染病、营养学和公共卫生受益。