Multivariate Statistcs

该研究项目涉及统计科学的几个领域，即几何统计，捕获-再捕获实验，调查抽样和copula建模。 它建议进行新的统计模型的理论研究和实际实施，以应对数据分析的现代挑战。首先，将开发随机效应循环回归模型，其动机是需要量化生态学中动物群体之间的关联。 然后，随机模型的树枝的树，被认为是一个层次的一组线性段，将被用来描述一棵树的结构，并调查它是如何与其他树的特征，如它的树叶。 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，copula模型将被用来构造新的小区域估计方法和项目无应答的新插补技术。

This research project is concerned with several areas of the statistical sciences, namely geometric statistics, capture-recapture experiments, survey sampling and copula modeling. It proposes to undertake the theoretical investigations, and the practical implementation, of new statistical models in response to modern challenges in data analysis. First random effects circular regression models will be developed, motivated by the need to quantify the association between animals traveling in groups in ecology. Then stochastics models for the branches of a tree, considered as a hierarchical set of linear segments, will be used to characterize the structure of a tree and to investigate how it is related to other tree's characteristics, such as its foliage. 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 methods and new techniques of imputation for item non response.