Flexible and Adaptive Statistical Modeling

灵活且自适应的统计建模

• 批准号: 1208164
• 负责人: Robert Tibshirani
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208164&HistoricalAwards=false
• 关键词: Flexible; Adaptive; Statistical; Modeling

The investigator and his colleagues will study selected topics in high-dimensional regression, classification and hypothesis testing. Specifically, these include a sparse factor model for high-dimensional regression and survival analysis, large scale interaction testing with applications to genomics, the computational algorithms for the fused lasso, and a research monograph on L1-constrained (Lasso) methods in statistics.This work will help scientists working in biotech and other areas, who generate large scale datasets, to interpret and uncover the important patterns in their data. This should help scientists and doctors to discover the biological bases of many diseases, and improve prognosis and treatment selection for patients.

研究者和他的同事将在高维回归、分类和假设检验中选择主题进行研究。具体来说，这些包括用于高维回归和生存分析的稀疏因子模型，与基因组学应用的大规模相互作用测试，融合套索的计算算法，以及关于统计学中l1约束（lasso）方法的研究专著。这项工作将帮助在生物技术和其他领域工作的科学家，他们产生大规模的数据集，来解释和揭示他们数据中的重要模式。这将有助于科学家和医生发现许多疾病的生物学基础，并改善患者的预后和治疗选择。