Novel statistical methods for data with non-Euclidean geometric structure

非欧几何结构数据的新颖统计方法

• 批准号: DP220102232
• 负责人: Prof Andrew Wood
• 金额: $ 28.05万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220102232
• 关键词: Novel; statistical; methods; data; non

This project aims to develop new flexible regression models and classification algorithms, along with robust and efficient inference methods, applicable to a wide range of non-Euclidean data types which arise in many fields of science, business and technology. There are serious flaws with currently available methods of analysis for non-Euclidean data. This project expects to transform such analyses by providing new quantitative tools within a unifying framework. The anticipated project outcomes will be of mathematical interest and valuable in applications such as finance (predicting Australian stock returns); modelling electroencephalography data; Australian geochemical data, relating to sediments; and Australian X-ray tumour image data.

该项目旨在开发新的灵活的回归模型和分类算法，沿着强大而有效的推理方法，适用于科学，商业和技术的许多领域中出现的广泛的非欧几里德数据类型。目前可用的非欧几里德数据分析方法存在严重缺陷。该项目期望通过在统一框架内提供新的量化工具来改变这种分析。预期的项目成果将具有数学意义，并在金融（预测澳大利亚股票收益）、模拟脑电图数据、澳大利亚与沉积物有关的地球化学数据和澳大利亚X射线肿瘤图像数据等应用方面具有价值。