New Developments on Quantile Regression Analysis of Censored Data: Theory, Methodology and Computation

截尾数据分位数回归分析的新进展：理论、方法和计算

• 批准号: 1308960
• 负责人: Lan Wang
• 金额: $ 12万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1308960&HistoricalAwards=false
• 关键词: New; Developments; Quantile; Regression; Analysis

Quantile regression has recently emerged as a valuable semiparametric alternative to the popular Cox model for analyzing censored data. It directly models survival time; thus is easy to interpret. More importantly, it relaxes the proportional hazards constraint associated with the Cox model and is particularly powerful for heterogeneous data. Despite the remarkable recent progress, several important and challenging statistical problems remain unsolved. For example, there exists limited literature on censored quantile regression when the sample arises from an observational study and is not representative of the target population; when the censored data come from genomics studies involving high-dimensional covariates; or when random effects are present due to the incorporation of latent variables. Motivated by these challenging problems, this project will develop novel methodology, theory and algorithms, which have the potential to significantly advance the applications of censored quantile regression. The PI will rigorously study the theoretical properties of the proposed new procedures and investigate their applications in practical data analysis. Censored data arise in diverse fields such as economics, engineering, medicine, psychology and sociology. The new methodology and theory are expected to make important contributions to the current body of knowledge on statistical analysis of survival data. In particular, the proposed research will make timely contributions to high-dimensional data analysis with censored responses, which has important applications in modern genomics and is still a relatively unexplored research area. The PI will develop useful software packages and make them freely available to the research community. The research results will be incorporated in different levels of statistical courses. The PI will also incorporate her research activity with graduate education. Students from minority groups will be especially encouraged to participate in the proposed projects.

分位数回归最近已成为流行的COX模型的有价值的半参数替代品，用于分析审查数据。它直接建模生存时间；因此很容易解释。更重要的是，它放松了与COX模型相关的比例危害约束，并且对于异质数据特别有力。 尽管最近取得了显着进展，但一些重要且具有挑战性的统计问题仍未解决。 例如，当样本是由观察性研究产生的，不代表目标人群时，对审查的分位数回归的文献有限。审查数据来自涉及高维协变量的基因组学研究；或由于掺入潜在变量而存在随机效应时。由于这些具有挑战性的问题，该项目将开发新的方法论，理论和算法，这些方法有可能显着推动审查的分位数回归的应用。 PI将严格研究拟议的新程序的理论特性，并研究其在实际数据分析中的应用。 审查数据出现在经济学，工程，医学，心理学和社会学等各个领域。期望新的方法和理论对生存数据的统计分析的当前知识体系做出重要贡献。 特别是，拟议的研究将通过审查响应在现代基因组学中具有重要应用，并且仍然是一个相对尚未开发的研究领域，对高维数据分析做出了及时贡献。 PI将开发有用的软件包，并使研究社区免费使用。研究结果将纳入不同级别的统计课程。 PI还将将她的研究活动与研究生教育结合起来。 特别鼓励来自少数群体的学生参加拟议的项目。