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

在今天的golobal村，复杂的数据已经变得很容易实时获得。这导致了更高的要求，在寻找有效的统计方法，估计和监督参数的数据。在这个项目中，我提出了有效的方法，将事先的非样本信息（如专家意见）的统计估计和检验程序。 这些方法将改善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式。我的建议的第二个目标是建立统计方法，用于基于呈现时间相关性的结果来监测参数。这些方法可应用于传染病的实时监测、卫生保健绩效监测和金融市场监测。

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.