Multivariate Models and Methods for Correlated Data in Non-Standard Settings

非标准设置中相关数据的多元模型和方法

• 批准号: 261821-2012
• 负责人: deLeon, Alexander
• 金额: $ 0.87万
• 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: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=525387
• 关键词: Multivariate; Models; Methods; Correlated; Data

The advent of sophisticated tools of measurement has given rise to new modes of data collection resulting in very large complex data, oftentimes involving mixed discrete and continuous variables or high-dimensional binary data with complex multi-level correlations. Their 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.

复杂测量工具的出现催生了新的数据收集模式，从而产生非常大的复杂数据，通常涉及混合的离散和连续变量或具有复杂多级相关性的高维二进制数据。 它们的复杂结构通常需要非标准统计方法，这些方法通常需要计算密集型方法。 这些在工程、金融、医学和健康领域的许多应用中尤其常见。 传统工具通常依赖于数据或其某些适当的变换遵循高斯分布的假设，并不直接适用于这些情况。