Models, methods and inference for non-standard correlated data

非标准相关数据的模型、方法和推理

• 批准号: RGPIN-2018-04748
• 负责人: deLeon, Alexander
• 金额: $ 1.17万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=712612
• 关键词: Models; methods; inference; non; standard

The advent of sophisticated tools of measurement has given rise to new modes of data collection resulting in non-standard correlated data, oftentimes involving disparate responses, frequently with a mixture of discrete and continuous responses, or of longitudinal and survival outcomes, and possibly from multiple sources or different study designs. The resulting complex structure typically requires non-standard statistical approaches that usually entail computationally intensive methodologies. These are particularly common in many applications in engineering, finance, and in medicine and health. Conventional tools that generally rely on the assumption that the data, or some suitable transformations of them, follow a Gaussian distribution, do not directly apply in these contexts. The research proposed here concerns the development of joint models and methodologies for application in non-standard correlated data settings. It pays specific focus on situations involving complex dependence structures arising from data comprising possibly high-dimensional non-standard correlated responses. Particular emphasis is given on development and computational implementation of new methodologies for use by practitioners in engineering and the medical/health sciences. The proposed research is anticipated to yield improved, flexible, and powerful techniques for data analysis in the age of big data.

复杂测量工具的出现产生了新的数据收集模式，导致非标准相关数据，通常涉及不同的反应，经常是离散和连续反应的混合，或纵向和生存结果，可能来自多个来源或不同的研究设计。由此产生的复杂结构通常需要非标准的统计方法，这些方法通常需要计算密集型的方法。这些在工程、金融、医学和健康领域的许多应用中特别常见。传统的工具通常依赖于假设数据，或者对数据进行一些适当的转换，遵循高斯分布，而这些工具并不直接适用于这些情况。