Computer Intensive Methods in Sampling and in Adaptive Contexts

采样和自适应环境中的计算机密集型方法

• 批准号: RGPIN-2016-05686
• 负责人: Léger, Christian
• 金额: $ 1.09万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635775
• 关键词: Computer; Intensive; Methods; Sampling; Adaptive

Fast computing has allowed statisticians to develop new statistical methods that would have been unthinkable before due to the impossibility to use them in practice. One example is the bootstrap. It can be used to compute variance estimates for complicated estimators or construct confidence intervals. The main goal of this research program is to improve the theoretical and practical understanding of these methods to develop sound new statistical procedures.

快速计算使统计学家能够开发新的统计方法，这些方法在以前是不可想象的，因为不可能在实践中使用它们。一个例子是bootstrap。它可用于计算复杂估计量的方差估计或构造置信区间。该研究计划的主要目标是提高对这些方法的理论和实践理解，以开发合理的新统计程序。