CRC in Statistical Sampling and Data Analysis

该研究项目将提供统计工具，以应对数据分析方面的现代挑战。 它涉及几何统计，捕获-再捕获实验，调查抽样和copula建模。首先，随机效应循环回归模型将被开发的大型数据集的动物运动，现在在生态学分析的动机。 然后，树的分支的随机模型，被认为是线性段的分层集合，将表征树的结构。 它们将用于分析树木扫描数据，现在可以提供树木3D结构的准确描述。将开发分析具有数百个捕获场合的大型捕获再捕获数据集的特殊方法，以分析由移动的电话上的应用程序激活生成的数据集。在调查抽样中，将使用copula模型来构建新的小面积估计技术，这是因为在编制调查估计数时需要纳入行政数据。

This research project will provide statistical tools to respond to modern challenges in data analysis. It is concerned with geometric statistics, capture-recapture experiments, survey sampling and copula modeling. First random effects circular regression models will be developed motivated by the analysis of the large data sets on animal movement that are now available in ecology. Then stochastics models for the branches of a tree, considered as a hierarchical set of linear segments, will characterize the structure of a tree. They will be used to analyze tree scanning data that now provides an accurate description of a tree 3-D structure. Special methods to analyze large capture recapture data sets featuring hundreds of capture occasions will be developed, to analyze data sets generated by the activation of applications on mobile phones. In survey sampling, copula models will be used to construct new small area estimation techniques motivated by the need to incorporate administrative data when producing survey estimates.