"Group sequential procedures based on Ranked Set Sampling, sequential change-point and shrinkage estimation in correlated data"

“基于相关数据中的排序集采样、顺序变化点和收缩估计的分组顺序过程”

• 批准号: 293251-2012
• 负责人: Hussein, Abdulkadir
• 金额: $ 0.87万
• 依托单位: University of Windsor
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=594852
• 关键词: Group; sequential; procedures; based; Ranked

In today's golobal village, complex data has become easily available in real-time. This has led to a higher demand in finding efficient statistical methods for estimation and surveillance of parameters governing the data. In this project, I propose efficient methods which incorporate prior nonsample information (such as expert opinion) in the statistical estimation and testing procedures. These methodlogies will improve the ways we tackle analysis of data from clinical trials, longitudinal, spatial and cluster correlated data. A second objective of my proposal is to build statistical methods for the surveillance of parameters based on outcomes that present temporal correlations. These methods have applications in real-time surveillance of infectious diseases, health care performace monitoring and in monitoring financial markets.

在当今的全球村庄中，复杂的数据已变得可以轻松实时获取。这导致对寻找有效的统计方法来估计和监视数据控制参数的需求更高。在这个项目中，我提出了有效的方法，将先前的非样本信息（例如专家意见）纳入统计估计和测试程序。这些方法将改进我们处理临床试验数据、纵向、空间和聚类相关数据分析的方法。我建议的第二个目标是建立统计方法，根据呈现时间相关性的结果来监视参数。这些方法可应用于传染病的实时监测、医疗保健绩效监测和金融市场监测。