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

这是一个为期两年的合作研究项目， Michael G. Akritas，宾夕法尼亚州统计局 和医学系Willem阿尔伯斯教授 荷兰林布尔格大学信息学和统计学。 数学家们正在合作进行一项关于检验和估算的研究 基于距离测量， bootstrap技术，以及bootstrap在某些 包括多重比较的排名程序。 这项工作可能会导致 发展一个共同的理论，用于测试和评估， 审查和未经审查的数据，因此与数据产生的 可靠性、寿命测试和医学研究。 一些 将考虑的性能方面是：对尾部的敏感性 替代品和交叉危险的替代品的一个和两个- 样本问题，分别和稳健性的估计，以及如何 这取决于审查的程度。 工作的第二个方面 关注的是引导技术的应用， 近似零分布或零方差的一些共同的 有或没有删失的秩统计，以及基于秩的多重 使用删失和未删失数据进行比较。 这项研究涉及发展和完善统计 进行“拟合优度”检验的程序， 所选模型与实验数据的拟合程度 意见。 特别是，程序的应用将 数据被“删失”的情况下，即， 一些原因，当一个病人退出临床研究之前， 研究结束了。 这些方法将允许比较 一种疾病的两种或多种治疗方法的有效性，例如， 并指出应该对 得出的结论。 所谓的“引导”技术， 相对较新和强大的统计工具，将在这方面应用 研究以获得真实概率分布的近似值， 感兴趣的参数。 阿克里塔斯教授有丰富的经验 将统计方法应用于删失数据。 阿尔伯斯教授是 公认的渐近理论权威，特别强调 非参数方法 两名研究人员合作， 这项工作的许多数学和计算方面应该大大 促进新程序的制定， 与现有的统计方法进行比较。

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.