CRC in Statistical Sampling and Data Analysis

统计采样和数据分析中的 CRC

• 批准号: CRC-2016-00164
• 负责人: Rivest, LouisPaul
• 金额: $ 14.57万
• 依托单位: Université Laval
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=694306
• 关键词: CRC; Statistical; Sampling; Data; Analysis

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.

这一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在分析目前生态学中可获得的动物运动的大量数据集的基础上开发。然后，树的分支的随机模型被认为是一组分层的线性段，将表征树的结构。它们将被用来分析树木扫描数据，这些数据现在提供了对树木三维结构的准确描述。将开发特殊方法来分析具有数百次捕获时机的大型捕获重新捕获数据集，以分析手机上激活应用程序所生成的数据集。在调查抽样中，将使用Copula模型来构建新的小区域估计技术，其动机是在产生调查估计时需要纳入行政数据。