Multivariate models and inference

多元模型和推理

• 批准号: 8698-2010
• 负责人: Joe, Harry
• 金额: $ 1.75万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=474430
• 关键词: Multivariate; models; inference

Classical statistical methods for multivariate statistics and time series have an assumption that the distributions of observations has a bell-shaped curve. Multivariate statistics consists of methods for analyzing data that has the form of many different numerical measurements or variables for many different subjects or cases; one inference of interest is the amount of association or relationship among different variables. Also a form of dependence called tail dependence is very important to study when the variables are related to financial and insurance risks. Time series consists of methods for analyzing data of the form of measurements of a main variable obtained sequentially over time and possibly other co-variables that are measured simultaneously; one inference of interest is to predict or forecast future values of the time series.

多元统计和时间序列的经典统计方法假设观测值的分布具有钟形曲线。 多变量统计学包括用于分析数据的方法，这些数据具有许多不同的数字测量或变量的形式，用于许多不同的主题或案例;一个感兴趣的推断是不同变量之间的关联或关系的数量。 此外，当变量与金融和保险风险相关时，一种称为尾部依赖的依赖形式对于研究非常重要。 时间序列包括用于分析主要变量的测量形式的数据的方法，这些测量是随着时间的推移顺序获得的，并且可能是同时测量的其他协变量;感兴趣的一个推断是预测或预测时间序列的未来值。