Bayesian Inference for Multivariate Hierarchical Regression Models

多元分层回归模型的贝叶斯推理

• 批准号: DP0453237
• 负责人: Prof Robert Kohn
• 金额: $ 19.85万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2004
• 资助国家: 澳大利亚
• 起止时间: 2004-01-01 至 2008-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0453237
• 关键词: Bayesian; Inference; Multivariate; Hierarchical; Regression

This project will develop Bayesian methodology for analysing multivariate regression models. The distribution of each measurement can be discrete or continuous, with the dependence between measurements obtained through the correlation matrix of a Gaussian copula. Model parsimony is obtained by identifying zero elements in the correlation matrix or its inverse and by variable selection on the regression parameters. The results will be applied to solve problems in finance, health management and marketing. In all these fields multiple observations are often taken per individual or time period and the models need to incorporate measures of dependence and uncertainty.

该项目将开发用于分析多元回归模型的贝叶斯方法。每个测量值的分布可以是离散的或连续的，测量值之间的依赖性是通过高斯联结函数的相关矩阵获得的。模型简约性是通过识别相关矩阵或其逆矩阵中的零元素以及通过回归参数的变量选择来获得的。研究结果将应用于解决财务、健康管理和营销方面的问题。在所有这些领域中，通常对每个人或每个时间段进行多次观察，并且模型需要纳入依赖性和不确定性的测量。