Statistical Methods for Some Applied Problems

一些应用问题的统计方法

• 批准号: 0102529
• 负责人: Yehuda Vardi
• 金额: $ 17.22万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0102529&HistoricalAwards=false
• 关键词: Statistical; Methods; Some; Applied; Problems

Abstract DMS 0102529 ---------- STATISTICAL METHODS FOR SOME APPLIED PROBLEMSStochastic models and methods for a number of real-life problems will be developed together with appropriate statistical inference tools. Results of the proposed research will be applicable and directly relevant to important areas of applications. The first topic concerns statistical methodology for problems originating by users' command streams in a multi-users computer network. Statistical modeling of such data has important applications in networks' intrusion-detection, in designing intelligent computer/internet environments with learning capabilities, and more. The goal is to develop a practical methodology for profiling individual users or, more generally, random sequences of commands originating from a fixed given source. Our methods allow the system to recognize 'statistical signature' of users, and to flag out masqueraders who might assume the e-identity of a legitimate user. The data pose huge practical challenges, because of its shear size and complexion and our current method has very good operating characteristics(false- and missing- alarms) on two publicly available test data. The second topic concerns the development of a new concept of data-depth functions, based on multivariate medians. We provided a new algorithm for calculating an important multivariate median function, and a closed form formula for the associated data depth. The methodology will lead to robust, practical tools for multivariate data analysis, inference, regression, image processing, and more. The third topic concerns regression analysis and nonparametric methods for the comparisons of growth curves under informative heterogeneous censoring. In many tumor growth inhibition studies, tumor sizes are recorded over a period of time, forming a growth curve for each experimental subject. The usual noninformative-censoring model is often not applicable, because subjects could be censored out of the study due to treatments toxicity effects. We propose to develop statistical tests for the comparison of tumor growth rates and estimates of regression coefficients, in the presence of informative heterogeneous censoring. The proposed testing procedures are expected to be widely used, since they naturally correct the censorship bias, retain high efficiency and require no distributional assumption of the growth curves or the censoring mechanism.

摘要DMS 0102529 - 一些应用问题的统计方法一些现实生活中的问题的随机模型和方法将与适当的统计推断工具一起开发。拟议研究的结果将适用于重要的应用领域，并与之直接相关。 第一个主题是关于多用户计算机网络中由用户命令流引起的问题的统计方法。 这些数据的统计建模在网络入侵检测、设计具有学习能力的智能计算机/互联网环境等方面具有重要的应用。 目标是开发一种实用的方法，用于分析单个用户，或者更一般地说，分析来自固定给定来源的随机命令序列。我们的方法允许系统识别用户的“统计签名”，并标记出可能承担合法用户的电子身份的masqueraders。数据带来了巨大的实际挑战，因为它的剪切大小和复杂性，我们目前的方法具有非常好的操作特性（假警报和漏警报）的两个公开的测试数据。第二个主题是基于多元中位数的数据深度函数的新概念的发展。我们提供了一个新的算法来计算一个重要的多元中值函数，和一个封闭的形式公式相关联的数据深度。 该方法将为多变量数据分析，推理，回归，图像处理等提供强大，实用的工具。 第三个主题是关于在信息异质删失下比较生长曲线的回归分析和非参数方法。在许多肿瘤生长抑制研究中，在一段时间内记录肿瘤大小，形成每个实验对象的生长曲线。通常的无信息删失模型通常不适用，因为受试者可能因治疗毒性效应而被删失出研究。我们建议在信息异质性删失的存在下，开发用于肿瘤生长率和回归系数估计的比较的统计检验。建议的测试程序，预计将被广泛使用，因为他们自然纠正审查的偏见，保持高效率，并要求没有分布假设的增长曲线或审查机制。