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.

该项目旨在开发对大型复杂多元纵向数据进行统计分析的新方法，并将其应用于两个数据集，以解决与澳大利亚身体和精神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。该项目期望在统计模型构建、模型选择和多元纵向数据推断等领域开发新知识。这将导致一套计算效率高、数学严谨的统计数据分析的现代方法和见解，这些方法和见解一经应用，将为公共卫生和生态带来重大好处。