U.S.-Netherlands Cooperative Research: Statistical Methods for Analyzing Data Arising from Reliability Studies (Mathematical Sciences)

美国-荷兰合作研究：分析可靠性研究数据的统计方法（数学科学）

• 批准号: 8700734
• 负责人: Michael Akritas
• 金额: $ 0.46万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1987
• 资助国家: 美国
• 起止时间: 1987-10-01 至 1990-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8700734&HistoricalAwards=false
• 关键词: U.S.; Netherlands; Cooperative; Research; Statistical

This is a two-year cooperative research project between Professor Michael G. Akritas, Department of Statistics, The Pennsylvania State University, and Professor Willem Albers, Department of Medical Informatics and Statistics, University of Limburg, The Netherlands. The mathematicians are cooperating in a study of tests and estimators that are based on distance measures, the associated use of the bootstrap technique, and the application of the bootstrap to certain rank procedures including multiple comparisons. The work may lead to the development of a common theory for testing and estimation with both censored and uncensored data, and so is relevant to data arising from reliability, life testing, and medical studies. Some of the performance aspects that will be considered are: sensitivity to tail alternatives and to crossing hazards alternatives for the one- and two- sample problem, respectively, and robustness of the estimators and how this depends on the degree of censoring. The second aspect of the work is concerned with applications of the bootstrap technique to approximating the null distribution or the null variance of some common rank statistics with or without censoring, and to rank-based multiple comparison procedures with both censored and uncensored data. This study is concerned with developing and refining statistical procedures for making "goodness of fit" tests that permit one to judge how well a chosen model fits data derived from experimental observations. In particular, applications of the procedures will be made to situations in which data are "censored," that is, missing for some reason, as when a patient drops out of a clinical study before the study is over. The methods would permit a comparison of the effectiveness of two or more treatments for a disease, for example, as well as indicate the levels of confidence that should be placed on the conclusions that are drawn. The so-called "bootstrap" technique, a relatively new and powerful statistical tool, will be applied in this study to obtain approximations to the true probability distribution of parameters of interest. Professor Akritas has considerable experience in applying statistical methods to censored data. Professor Albers is a recognized authority in asymptotic theory with special emphasis in nonparametric methods. Collaboration by the two researchers on the many mathematical and computational aspects of this work should greatly facilitate the development of the new procedures as well as their comparison with existing statistical methods.

这是宾夕法尼亚州立大学统计系Michael G.Akritas教授和荷兰林堡大学医学信息学和统计系Willem Albers教授之间为期两年的合作研究项目。数学家们正在合作研究基于距离测量的测试和估计器，Bootstrap技术的相关使用，以及Bootstrap在包括多次比较在内的特定等级程序中的应用。这项工作可能会导致发展一种共同的理论，用于对删失和未删失数据进行测试和估计，因此与可靠性、寿命测试和医学研究中的数据相关。将考虑的一些性能方面是：对单样本和两样本问题的尾部选择和交叉风险选择的敏感性，以及估计器的稳健性以及这如何依赖于审查程度。工作的第二个方面涉及Bootstrap技术在有或没有删失的情况下逼近一些常见秩统计量的零分布或零方差的应用，以及在删失和未删失数据下的基于秩次的多重比较过程。这项研究致力于开发和改进统计程序，以进行“拟合优度”测试，使人们能够判断所选模型与实验观测数据的拟合程度。特别是，这些程序将被应用于数据被“审查”的情况，即由于某种原因而丢失的情况，例如当患者在研究结束前退出临床研究时。例如，这些方法将允许对一种疾病的两种或两种以上治疗方法的有效性进行比较，并表明应对得出的结论给予的置信度。所谓的“自举”技术，一个相对新的和强大的统计工具，将被应用在这项研究中，以获得对感兴趣参数的真实概率分布的近似。Akritas教授在将统计方法应用于审查数据方面拥有相当丰富的经验。阿尔伯斯教授是渐近理论的公认权威，特别强调非参数方法。两位研究人员在这项工作的许多数学和计算方面开展合作，应极大地促进新程序的制定及其与现有统计方法的比较。