LINEAR METHODS IN STATISTICS

统计学中的线性方法

• 批准号: 3916953
• 负责人: J D MALLEY
• 金额: --
• 依托单位: CENTER FOR INFORMATION TECHNOLOGY
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/3916953
• 关键词: artificial intelligence; genetic models; mathematical model; mathematics; model design /development; statistics /biometry

The objective of this continuing project is to develop new-methods in statistics, using algebraic techniques, that have applications for biomedical research. Newly developed methods in variance component estimation, and estimation in models with linear covariance structure, were previously brought together and published in a book on the subject. Research on these methods continues, particularly with respect to applications in genetics, and animal sciences. The algebraic methods themselves continue to be studied with one paper being published in the past year, while another is accepted for publication. Further studies concern the use of Jordan algebras to solve the problem of estimation in the presence of missing data in the mixed linear model. This technique applies to repeated measures problems and longitudinal studies, both of much interest to the biomedical community.

这个持续项目的目标是开发新的方法 在统计学中，使用代数技术，具有应用 用于生物医学研究。 新开发的方差分量估计方法，以及 在具有线性协方差结构的模型中的估计， 以前汇集在一起，并出版了一本关于这个主题的书。 对这些方法的研究仍在继续，特别是关于 在遗传学和动物科学方面的应用。代数论 方法本身继续被研究，有一篇论文是 在过去一年出版的，而另一本则被接受为 出版。进一步的研究涉及到Jordan代数的使用 解决缺失数据情况下的估计问题 在混合线性模型中。这项技术适用于重复 测量问题和纵向研究，两者都很感兴趣 给生物医学界。