Modern statistical methods for complex multivariate longitudinal data

复杂多元纵向数据的现代统计方法

• 批准号: DE200100435
• 负责人: Dr Francis Hui
• 金额: $ 25.63万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Early Career Researcher Award
• 财政年份: 2020
• 资助国家: 澳大利亚
• 起止时间: 2020-01-15 至 2024-01-14
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DE200100435
• 关键词: Modern; statistical; methods; complex; multivariate

The project aims to develop novel approaches for the statistical analysis of large, complex multivariate longitudinal data, and apply them to two datasets to address scientific questions related to the drivers and consequences of poor physical and mental health in Australia, and the spatio-temporal evolution of species assemblages in the Southern Ocean. The project expects to develop new knowledge in the areas of statistical model building, model selection, and inference for multivariate longitudinal data. This will lead to a suite of modern methods and insights for computationally efficient, mathematically rigorous statistical data analysis that, when applied, should provide significant benefits to public health and ecology.

该项目旨在为大型复杂多元纵向数据的统计分析开发新方法，并将其应用于两个数据集，以解决与澳大利亚身心健康状况不佳的驱动因素和后果以及南大洋物种组合的时空演变有关的科学问题。该项目预计将开发新的知识领域的统计模型的建设，模型选择和推断多变量纵向数据。这将产生一套现代方法和见解，用于计算效率高，数学上严格的统计数据分析，当应用时，应该为公共卫生和生态提供显着的好处。