Sufficient Dimension Reduction for Missing, Censored, and Correlated Data

针对缺失、删失和相关数据进行充分降维

• 批准号: 0706919
• 负责人: Lexin Li
• 金额: $ 11.99万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0706919&HistoricalAwards=false
• 关键词: Sufficient; Dimension; Reduction; Missing; Censored

This proposal aims to develop new theory and methodology for sufficient dimension reduction (SDR). In particular, the research focuses on biostatistical problems which commonly include missing data, survival data, and longitudinal data analysis. The proposed methodology can effectively transform a high dimensional regression problem to a low dimensional projection, retain full regression information, and impose few or no probabilistic models. There are three components to this research. First, the investigator proposes a family of augmented inverse probability weighted SDR estimators when predictors have missing observations. This new approach allows a more general missing data mechanism than the existing solution and permits more flexible regression forms beyond the homoscedastic linear model. The second component of the research targets SDR for survival data, where the response of time to death or disease recurrence is subject to censoring. Viewing the censored response as a specific type of missing data, the investigator integrates an inverse probability weighted estimation strategy with a variety of SDR methods. Thirdly, the investigator studies a type of longitudinal data where measurements for all the study subjects are collected at the same scheduled time points. Both a population foundation and the associated estimation procedure are developed. All three components center around commonly encountered biostatistical problems and the development of the three components are interrelated.Modern technologies have pushed the frontier of science with the capability of generating and collecting data in large quantity and high dimensionality. Examples of large high dimensional data sets arise in a great number of research areas, such as environmental studies, human health and medical research, and homeland security. Sufficient dimension reduction (SDR) methodology effectively transforms a high dimensional data problem to a low dimensional one. Consequently, SDR allows many existing analytical methods, which used to be hindered by the curse of dimensionality, to now work for the high dimensional problems. In addition, informative visualization of the data often becomes possible after dimension reduction, facilitating both the understanding and the analysis of the data. By developing new theory and methodology for missing, censored, and correlated data, the investigator's research extends the boundary of SDR to biostatistics as well as other disciplines such as econometrics, finance and bioinformatics. The impact of this research is anticipated to be widespread, due to the prevalence of the high dimensional data and the urgent demand for effective analytical tools to tackle those problems.

该提案旨在开发新的理论和方法来实现充分降维（SDR）。该研究特别关注生物统计学问题，通常包括缺失数据、生存数据和纵向数据分析。所提出的方法可以有效地将高维回归问题转换为低维投影，保留完整的回归信息，并且施加很少或不施加概率模型。这项研究由三个部分组成。首先，当预测变量缺少观测值时，研究者提出了一系列增强逆概率加权 SDR 估计器。这种新方法允许比现有解决方案更通用的缺失数据机制，并允许超出同方差线性模型的更灵活的回归形式。该研究的第二个组成部分针对生存数据的 SDR，其中对死亡或疾病复发的时间响应受到审查。研究人员将截尾响应视为特定类型的缺失数据，将逆概率加权估计策略与各种 SDR 方法相结合。第三，研究者研究一种纵向数据，其中所有研究对象的测量值都是在相同的预定时间点收集的。人口基础和相关的估计程序都已制定。这三个组成部分都围绕常见的生物统计学问题展开，三个组成部分的发展是相互关联的。现代技术以生成和收集大量、高维数据的能力推动了科学的前沿。大型高维数据集的例子出现在许多研究领域，例如环境研究、人类健康和医学研究以及国土安全。充分降维（SDR）方法有效地将高维数据问题转化为低维数据问题。因此，SDR 使得许多过去受到维数灾难阻碍的现有分析方法现在可以用于解决高维问题。此外，降维后通常可以实现数据的信息可视化，从而促进数据的理解和分析。通过针对缺失、审查和相关数据开发新的理论和方法，研究人员的研究将 SDR 的边界扩展到生物统计学以及计量经济学、金融学和生物信息学等其他学科。由于高维数据的普遍存在以及对有效分析工具来解决这些问题的迫切需求，预计这项研究的影响将是广泛的。